WorldWideScience

Sample records for vector autoregressive var

  1. A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...

    African Journals Online (AJOL)

    This paper is an attempt to determine the presence and empirical significance of monetary policy and the bank lending view of the credit channel for Mauritius, which is particularly relevant at these times. A vector autoregressive (VAR) model of order three is used to examine the monetary transmission mechanism using ...

  2. Noncausal Bayesian Vector Autoregression

    DEFF Research Database (Denmark)

    Lanne, Markku; Luoto, Jani

    We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...

  3. METODE VECTOR AUTOREGRESSIVE (VAR DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI

    Directory of Open Access Journals (Sweden)

    TJOK GDE SAHITYAHUTTI RANANGGA

    2018-05-01

    Full Text Available The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan arrival to Bali using vector autoregressive (VAR method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6.We used multivariate least square method to estimate the VAR(6’s parameters.The mean absolute percentage error (MAPE in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tourists. The prediction of Australian, Chinese, and Japanese tourists arrival to Bali for July 2017 to December 2017 tended  to experience up and downs that were not too high compared to the previous months.

  4. iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models.

    Science.gov (United States)

    Liu, Siwei; Molenaar, Peter C M

    2014-12-01

    This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

  5. Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price

    Science.gov (United States)

    Suharsono, Agus; Aziza, Auliya; Pramesti, Wara

    2017-12-01

    Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship

  6. Debt Contagion in Europe: A Panel-Vector Autoregressive (VAR Analysis

    Directory of Open Access Journals (Sweden)

    Florence Bouvet

    2013-12-01

    Full Text Available The European sovereign-debt crisis began in Greece when the government announced in December, 2009, that its debt reached 121% of GDP (or 300 billion euros and its 2009 budget deficit was 12.7% of GDP, four times the level allowed by the Maastricht Treaty. The Greek crisis soon spread to other Economic and Monetary Union (EMU countries, notably Ireland, Portugal, Spain and Italy. Using quarterly data for the 2000–2011 period, we implement a panel-vector autoregressive (PVAR model for 11 EMU countries to examine the extent to which a rise in a country’s bond-yield spread or debt-to-GDP ratio affects another EMU countries’ fiscal and macroeconomic outcomes. To distinguish between interdependence and contagion among EMU countries, we compare results obtained for the pre-crisis period (2000–2007 with the crisis period (2008–2011 and control for global risk aversion.

  7. Oil Price Volatility and Economic Growth in Nigeria: a Vector Auto-Regression (VAR Approach

    Directory of Open Access Journals (Sweden)

    Edesiri Godsday Okoro

    2014-02-01

    Full Text Available The study examined oil price volatility and economic growth in Nigeria linking oil price volatility, crude oil prices, oil revenue and Gross Domestic Product. Using quarterly data sourced from the Central Bank of Nigeria (CBN Statistical Bulletin and World Bank Indicators (various issues spanning 1980-2010, a non‐linear model of oil price volatility and economic growth was estimated using the VAR technique. The study revealed that oil price volatility has significantly influenced the level of economic growth in Nigeria although; the result additionally indicated a negative relationship between the oil price volatility and the level of economic growth. Furthermore, the result also showed that the Nigerian economy survived on crude oil, to such extent that the country‘s budget is tied to particular price of crude oil. This is not a good sign for a developing economy, more so that the country relies almost entirely on revenue of the oil sector as a source of foreign exchange earnings. This therefore portends some dangers for the economic survival of Nigeria. It was recommended amongst others that there should be a strong need for policy makers to focus on policy that will strengthen/stabilize the economy with specific focus on alternative sources of government revenue. Finally, there should be reduction in monetization of crude oil receipts (fiscal discipline, aggressive saving of proceeds from oil booms in future in order to withstand vicissitudes of oil price volatility in future.

  8. Empirical Vector Autoregressive Modeling

    NARCIS (Netherlands)

    M. Ooms (Marius)

    1993-01-01

    textabstractChapter 2 introduces the baseline version of the VAR model, with its basic statistical assumptions that we examine in the sequel. We first check whether the variables in the VAR can be transformed to meet these assumptions. We analyze the univariate characteristics of the series.

  9. Model reduction methods for vector autoregressive processes

    CERN Document Server

    Brüggemann, Ralf

    2004-01-01

    1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo­ cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo­ sitions, have been developed over the years. The econometrics of VAR models and related quantities i...

  10. Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

    Science.gov (United States)

    Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

    2016-01-01

    Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing

  11. Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach

    International Nuclear Information System (INIS)

    Xu, Bin; Lin, Boqiang

    2015-01-01

    Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption. China's transport sector is highly energy-consuming and pollution-intensive. Between 1980 and 2012, the carbon dioxide emissions in China's transport sector increased approximately 9.7 times, with an average annual growth rate of 7.4%. Identifying the driving forces of the increase in carbon dioxide emissions in the transport sector is vital to developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the sector. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Private vehicles have more impact on emission reduction than cargo turnover due to the surge in private car population and its low energy efficiency. Urbanization also has significant effect on carbon dioxide emissions because of large-scale population movements and the transformation of the industrial structure. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the transport sector. - Highlights: • The driving forces of CO 2 emissions in China's transport sector were investigated. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to large-scale migration. • The role of private cars in reducing emissions is more important than cargo turnover

  12. MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

    Directory of Open Access Journals (Sweden)

    D. Tutberidze

    2017-04-01

    Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

  13. Optimal Hedging with the Vector Autoregressive Model

    NARCIS (Netherlands)

    L. Gatarek (Lukasz); S.G. Johansen (Soren)

    2014-01-01

    markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be

  14. The Integration Order of Vector Autoregressive Processes

    DEFF Research Database (Denmark)

    Franchi, Massimo

    We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2...

  15. Oracle Inequalities for High Dimensional Vector Autoregressions

    DEFF Research Database (Denmark)

    Callot, Laurent; Kock, Anders Bredahl

    This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order...

  16. Use of (Time-Domain) Vector Autoregressions to Test Uncovered Interest Parity

    OpenAIRE

    Takatoshi Ito

    1984-01-01

    In this paper, a vector autoregression model (VAR) is proposed in order to test uncovered interest parity (UIP) in the foreign exchange market. Consider a VAR system of the spot exchange rate (yen/dollar), the domestic (US) interest rate and the foreign (Japanese) interest rate, describing the interdependence of the domestic and international financia lmarkets. Uncovered interest parity is stated as a null hypothesis that the current difference between the two interest rates is equal to the d...

  17. Circular Conditional Autoregressive Modeling of Vector Fields.

    Science.gov (United States)

    Modlin, Danny; Fuentes, Montse; Reich, Brian

    2012-02-01

    As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.

  18. Estimation of pure autoregressive vector models for revenue series ...

    African Journals Online (AJOL)

    This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...

  19. Bias-correction in vector autoregressive models

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    2014-01-01

    We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...

  20. Dealing with Multiple Solutions in Structural Vector Autoregressive Models.

    Science.gov (United States)

    Beltz, Adriene M; Molenaar, Peter C M

    2016-01-01

    Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

  1. Likelihood inference for a fractionally cointegrated vector autoregressive model

    DEFF Research Database (Denmark)

    Johansen, Søren; Ørregård Nielsen, Morten

    2012-01-01

    such that the process X_{t} is fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß'X_{t} is fractional of order d-b, and no other fractionality order is possible. We define the statistical model by 0inference when the true values satisfy b0¿1/2 and d0-b0......We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters...... process in the parameters when errors are i.i.d. with suitable moment conditions and initial values are bounded. When the limit is deterministic this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of (ß...

  2. vector bilinear autoregressive time series model and its superiority

    African Journals Online (AJOL)

    KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.

  3. A Dynamic Model of U.S. Sugar-Related Markets: A Cointegrated Vector Autoregression Approach

    OpenAIRE

    Babula, Ronald A.; Newman, Douglas; Rogowsky, Robert A.

    2006-01-01

    The methods of the cointegrated vector autoregression (VAR) model are applied to monthly U.S. markets for sugar and for sugar-using markets for confectionary, soft drink, and bakery products. Primarily a methods paper, we apply Johansen and Juselius' advanced procedures to these markets for perhaps the first time, with focus on achievement of a statistically adequate model through analysis of a battery of advanced statistical diagnostic tests and on exploitation of the system's cointegration ...

  4. Exchange rate pass-through in Switzerland: Evidence from vector autoregressions

    OpenAIRE

    Jonas Stulz

    2007-01-01

    This study investigates the pass-through of exchange rate and import price shocks to different aggregated prices in Switzerland. The baseline analysis is carried out with recursively identified vector autoregressive (VAR) models. The data set comprises monthly observations, and pass-through effects are quantified by means of impulse response functions. Evidence shows that the exchange rate pass-through to import prices is substantial (although incomplete), but only moderate to total consumer ...

  5. Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

    Science.gov (United States)

    Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

    2007-11-01

    In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

  6. Single-Index Additive Vector Autoregressive Time Series Models

    KAUST Repository

    LI, YEHUA; GENTON, MARC G.

    2009-01-01

    We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided

  7. Vector bilinear autoregressive time series model and its superiority ...

    African Journals Online (AJOL)

    In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

  8. Vector autoregressive model approach for forecasting outflow cash in Central Java

    Science.gov (United States)

    hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita

    2018-05-01

    Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.

  9. Modelling cointegration in the vector autoregressive model

    DEFF Research Database (Denmark)

    Johansen, Søren

    2000-01-01

    A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends are defined. The statistical model for cointegrated I(1) variables is defined, and it is shown how hypotheses on the cointegratin...

  10. A General Representation Theorem for Integrated Vector Autoregressive Processes

    DEFF Research Database (Denmark)

    Franchi, Massimo

    We study the algebraic structure of an I(d) vector autoregressive process, where d is restricted to be an integer. This is useful to characterize its polynomial cointegrating relations and its moving average representation, that is to prove a version of the Granger representation theorem valid...

  11. Optimal hedging with the cointegrated vector autoregressive model

    DEFF Research Database (Denmark)

    Gatarek, Lukasz; Johansen, Søren

    We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the...

  12. Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

    Science.gov (United States)

    Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis

    2018-01-01

    Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

  13. Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model

    DEFF Research Database (Denmark)

    Møller, Niels Framroze

    2008-01-01

    Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity in the econo......Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity...... are related to expectations formation, market clearing, nominal rigidities, etc. Finally, the general-partial equilibrium distinction is analyzed....

  14. Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model

    DEFF Research Database (Denmark)

    Møller, Niels Framroze

    2008-01-01

    Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity in the econo......Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity...... parameters of the CVAR are shown to be interpretable in terms of expectations formation, market clearing, nominal rigidities, etc. The general-partial equilibrium distinction is also discussed....

  15. Single-Index Additive Vector Autoregressive Time Series Models

    KAUST Repository

    LI, YEHUA

    2009-09-01

    We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

  16. Testing exact rational expectations in cointegrated vector autoregressive models

    DEFF Research Database (Denmark)

    Johansen, Søren; Swensen, Anders Rygh

    1999-01-01

    This paper considers the testing of restrictions implied by rational expectations hypotheses in a cointegrated vector autoregressive model for I(1) variables. If the rational expectations involve one-step-ahead observations only and the coefficients are known, an explicit parameterization...... of the restrictions is found, and the maximum-likelihood estimator is derived by regression and reduced rank regression. An application is given to a present value model....

  17. The impact of oil-price shocks on Hawaii's economy: A case study using vector autoregression

    International Nuclear Information System (INIS)

    Gopalakrishnan, C.; Tian, X.; Tran, D.

    1991-01-01

    The effects of oil-price shocks on the macroeconomic performance of a non-oil-producing, oil-importing state are studied in terms of Hawaii's experience (1974-1986) using Vector Autoregression (VAR). The VAR model contains three macrovariables-real oil price, interest rate, and real GNP, and three regional variable-total civilian labor force, Honolulu consumer price index, and real personal income. The results suggested that oil-price shock had a positive effect on interest rate as well as local price (i.e., higher interest and higher local price), but a negative influence on real GNP. The negative income effect, however, was offset by the positive employment effect. The price of oil was found to be exogenous to all other variables in the system. The macrovariables exerted a pronounced impact on Hawaii's economy, most notably on consumer price

  18. Temporal aggregation in first order cointegrated vector autoregressive models

    DEFF Research Database (Denmark)

    La Cour, Lisbeth Funding; Milhøj, Anders

    We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...

  19. Temporal aggregation in first order cointegrated vector autoregressive

    DEFF Research Database (Denmark)

    la Cour, Lisbeth Funding; Milhøj, Anders

    2006-01-01

    We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline....

  20. Price transmission for agricultural commodities in Uganda: An empirical vector autoregressive analysis

    DEFF Research Database (Denmark)

    Lassen Kaspersen, Line; Føyn, Tullik Helene Ystanes

    This paper investigates price transmission for agricultural commodities between world markets and the Ugandan market in an attempt to determine the impact of world market prices on the Ugandan market. Based on the realization that price formation is not a static concept, a dynamic vector...... price relations, i.e. the price variations between geographically separated markets in Uganda and the world markets. Our analysis indicates that food markets in Uganda, based on our study of sorghum price transmission, are not integrated into world markets, and that oil prices are a very determining...... autoregressive (VAR) model is presented. The prices of Robusta coffee and sorghum are examined, as both of these crops are important for the domestic economy of Uganda – Robusta as a cash crop, mainly traded internationally, and sorghum for consumption at household level. The analysis focuses on the spatial...

  1. Bias-correction in vector autoregressive models: A simulation study

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....

  2. The cointegrated vector autoregressive model with general deterministic terms

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Morten Ørregaard

    2017-01-01

    In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)=Z(t) Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are X 2 -distributed....

  3. The cointegrated vector autoregressive model with general deterministic terms

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Morten Ørregaard

    In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)= Z(t) + Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are khi squared distributed....

  4. Bias-Correction in Vector Autoregressive Models: A Simulation Study

    Directory of Open Access Journals (Sweden)

    Tom Engsted

    2014-03-01

    Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.

  5. Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)

    2017-01-15

    This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.

  6. Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

    International Nuclear Information System (INIS)

    Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

    2017-01-01

    This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.

  7. Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

    Science.gov (United States)

    Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

    2017-01-01

    This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

    Directory of Open Access Journals (Sweden)

    Alev Dilek Aydin

    2015-01-01

    Full Text Available The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

  9. Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.

    Science.gov (United States)

    Aydin, Alev Dilek; Caliskan Cavdar, Seyma

    2015-01-01

    The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

  10. Bayesian model averaging in vector autoregressive processes with an investigation of stability of the US great ratios and risk of a liquidity trap in the USA, UK and Japan

    NARCIS (Netherlands)

    R.W. Strachan (Rodney); H.K. van Dijk (Herman)

    2007-01-01

    textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent

  11. Thresholds and Smooth Transitions in Vector Autoregressive Models

    DEFF Research Database (Denmark)

    Hubrich, Kirstin; Teräsvirta, Timo

    This survey focuses on two families of nonlinear vector time series models, the family of Vector Threshold Regression models and that of Vector Smooth Transition Regression models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the...

  12. Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Kapetanios, George

    We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (...

  13. Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility

    DEFF Research Database (Denmark)

    Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert

    Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...

  14. Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, A. M. Robert

    Many key macro-economic and …nancial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...

  15. A representation theory for a class of vector autoregressive models for fractional processes

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    Based on an idea of Granger (1986), we analyze a new vector autoregressive model defined from the fractional lag operator 1-(1-L)^{d}. We first derive conditions in terms of the coefficients for the model to generate processes which are fractional of order zero. We then show that if there is a un...... root, the model generates a fractional process X(t) of order d, d>0, for which there are vectors ß so that ß'X(t) is fractional of order d-b, 0...

  16. Business cycles and fertility dynamics in the United States: a vector autoregressive model.

    Science.gov (United States)

    Mocan, N H

    1990-01-01

    "Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt

  17. Very-short-term wind power probabilistic forecasts by sparse vector autoregression

    DEFF Research Database (Denmark)

    Dowell, Jethro; Pinson, Pierre

    2016-01-01

    A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...

  18. Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Callot, Laurent

    We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...

  19. PENERAPAN MODEL ARBITRAGE PRICING THEORY DENGAN PENDEKATAN VECTOR AUTOREGRESSION DALAM MENGESTIMASI EXPECTED RETURN SAHAM (Studi Kasus: Saham-Saham Kompas100 Periode 2010-2013

    Directory of Open Access Journals (Sweden)

    VIAN RISKA AYUNING TYAS

    2014-01-01

    Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%

  20. PENERAPAN MODEL ARBITRAGE PRICING THEORY DENGAN PENDEKATAN VECTOR AUTOREGRESSION DALAM MENGESTIMASI EXPECTED RETURN SAHAM (Studi Kasus: Saham-Saham Kompas100 Periode 2010-2013

    Directory of Open Access Journals (Sweden)

    VIAN RISKA AYUNING TYAS

    2014-08-01

    Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%

  1. Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

    Science.gov (United States)

    Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

    Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes

  2. Sensor network based solar forecasting using a local vector autoregressive ridge framework

    Energy Technology Data Exchange (ETDEWEB)

    Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-04-04

    The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

  3. The impact of media campaigns on smoking cessation activity: a structural vector autoregression analysis.

    Science.gov (United States)

    Langley, Tessa E; McNeill, Ann; Lewis, Sarah; Szatkowski, Lisa; Quinn, Casey

    2012-11-01

    To evaluate the effect of tobacco control media campaigns and pharmaceutical company-funded advertising for nicotine replacement therapy (NRT) on smoking cessation activity. Multiple time series analysis using structural vector autoregression, January 2002-May 2010. England and Wales. Tobacco control campaign data from the Central Office of Information; commercial NRT campaign data; data on calls to the National Health Service (NHS) stop smoking helpline from the Department of Health; point-of-sale data on over-the-counter (OTC) sales of NRT; and prescribing data from The Health Improvement Network (THIN), a database of UK primary care records. Monthly calls to the NHS stop smoking helpline and monthly rates of OTC sales and prescribing of NRT. A 1% increase in tobacco control television ratings (TVRs), a standard measure of advertising exposure, was associated with a statistically significant 0.085% increase in calls in the same month (P = 0.007), and no statistically significant effect in subsequent months. Tobacco control TVRs were not associated with OTC NRT sales or prescribed NRT. NRT advertising TVRs had a significant effect on NRT sales which became non-significant in the seasonally adjusted model, and no significant effect on prescribing or calls. Tobacco control campaigns appear to be more effective at triggering quitting behaviour than pharmaceutical company NRT campaigns. Any effect of such campaigns on quitting behaviour seems to be restricted to the month of the campaign, suggesting that such campaigns need to be sustained over time. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

  4. How Rational Are Inflation Expectations? A Vector Autoregression Decomposition of Inflation Forecasts and Their Errors

    National Research Council Canada - National Science Library

    Ladvogt, Timothy

    2002-01-01

    ... the persistence of forecast errors. A reduced form VAR is used to identify potential inefficiencies and then calculate the impulse response functions and variance decompositions of forecasts errors to analyze how shocks to the other endogenous...

  5. A VAR Analysis Regarding Tax Evasion and Tax Pressure in Romania

    Directory of Open Access Journals (Sweden)

    Boștină Florin

    2017-01-01

    The main aim of the paper is to identify the relationship that exists between tax evasion and tax pressure in Romania, between 2000 and 2013, using an autoregressive vector type of analysis. The VAR model with 3 lags can be considered as representative in order to describe autoregressive links between tax evasion and fiscal pressure in Romania.

  6. Computing level-impulse responses of log-specified VAR systems

    NARCIS (Netherlands)

    Wieringa, J.E.; Horvath, C.

    2005-01-01

    Impulse response functions (IRFs) are often used to analyze the dynamic behavior of a vector autoregressive (VAR) system. In many applications of VAR modelling, the variables are log-transformed before the model is estimated. If this is the case, the results of the IRFs do not have a direct

  7. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available and Gertler, 1995), but also because changes in house prices tend to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), the importance of forecasting house price infl ation is vital, since... sections, respectively, lay out the DFM and outline the basics of the VAR, the Minnesota-type BVARs, and the SBVARs based on the fi rst-order spatial contiguity (FOSC) and the random walk averaging (RWA) priors developed by LeSage and Pan (1995) and Le...

  8. A Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching

    DEFF Research Database (Denmark)

    Haldrup, Niels; Nielsen, Frank; Nielsen, Morten Ørregaard

    2007-01-01

    A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the regime states as well as the possibility of fractional cointegra- tion. The model is relevant in describing the price dynamics of electricity prices where the transmission of power is subject...... to occasional congestion periods. For a system of bilat- eral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between essen- tially a univariate price process under non-congestion and a bivariate price...

  9. A vector autoregressive model for electricity prices subject to long memory and regime switching

    International Nuclear Information System (INIS)

    Haldrup, Niels; Nielsen, Frank S.; Nielsen, Morten Oerregaard

    2010-01-01

    A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models. (author)

  10. Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model

    DEFF Research Database (Denmark)

    Møller, Niels Framroze

    This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...

  11. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

    International Nuclear Information System (INIS)

    Che Jinxing; Wang Jianzhou

    2010-01-01

    In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

  12. Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period.

    Science.gov (United States)

    Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus

    2016-07-01

    Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.

  13. Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

    International Nuclear Information System (INIS)

    Xu, Bin; Lin, Boqiang

    2016-01-01

    Highlights: • We explore the driving forces of the iron and steel industry’s CO 2 emissions in China. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to mass real estate construction. • The role of economic growth in reducing emissions is more important than industrialization. - Abstract: Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO 2 ) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Urbanization also has significant effect on CO 2 emissions because of mass urban infrastructure and real estate construction. Economic growth has more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the iron and steel industry.

  14. On the detection of effective marketing instruments and causality in VAR models

    NARCIS (Netherlands)

    Horváth, C.; Otter, P.W.

    2000-01-01

    Dynamic multivariate models become more and more popular in analyzing the behavior of competive marketing environments. Takada and Bass (1998), Dekimpe, Hanssens and Silva-Rosso (1999), and Dekimpe and Hanssens (1999) recommend to use Vector Autoregressive (VAR) models because they provide

  15. Poisson Autoregression

    DEFF Research Database (Denmark)

    Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag

    2009-01-01

    In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies...... to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and past observations. As a particular example, we consider...... an exponential autoregressive Poisson model for time series. Under geometric ergodicity, the maximum likelihood estimators are shown to be asymptotically Gaussian in the linear model. In addition, we provide a consistent estimator of their asymptotic covariance matrix. Our approach to verifying geometric...

  16. Poisson Autoregression

    DEFF Research Database (Denmark)

    Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag

    This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time...

  17. Poisson Autoregression

    DEFF Research Database (Denmark)

    Fokianos, Konstantinos; Rahbæk, Anders; Tjøstheim, Dag

    This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, making an interpretation as an integer valued GARCH process possible. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model...

  18. Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

    Science.gov (United States)

    Levine, Matthew E; Albers, David J; Hripcsak, George

    2016-01-01

    Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

  19. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    Science.gov (United States)

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  20. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

    Directory of Open Access Journals (Sweden)

    Jaime Buitrago

    2017-01-01

    Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

  1. Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting

    DEFF Research Database (Denmark)

    Zhao, Yongning; Ye, Lin; Pinson, Pierre

    2018-01-01

    The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vec......The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity...... matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building...

  2. Some tests for parameter constancy in cointegrated VAR-models

    DEFF Research Database (Denmark)

    Hansen, Henrik; Johansen, Søren

    1999-01-01

    Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ......Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations......, and another in which the cointegrating relations are estimated recursively from a likelihood function, where the short-run parameters have been concentrated out. We suggest graphical procedures based on recursively estimated eigenvalues to evaluate the constancy of the long-run parameters in the model...

  3. Insecticidal effects of Ocimum sanctum var. cubensis essential oil on the diseases vector Chrysomya putoria

    Directory of Open Access Journals (Sweden)

    Idelsy Chil-Núñez

    2018-05-01

    Full Text Available Context: The blowfly Chrysomya putoria is widely distributed throughout the Neotropical region and, besides transmitting pathogens; they could cause secondary myiasis. Botanical insecticides provide an alternative to synthetic pesticides because the excessive use of synthetic insecticides resulted in a progressive resistance of the pests to these chemicals, diminishing their effectiveness and generating consequences with negative environmental impact. The essential oil extracted from Ocimum sanctum (basil has showed insecticidal activity against some insects but has no reported studies on the activity of this plant against flies. Aims: To evaluate the insecticidal effects of Ocimum sanctum var. cubensis Gomes essential oil on the post embryonic development of Chrysomya putoria. Methods: The colonies of Chrysomya putoria were established and maintained at the Laboratório de Entomologia Médica e Forense (FIOCRUZ, Rio de Janeiro, Brazil. The basil essential oil was tested in six concentrations (4.13, 8.25, 20.63, 41.25, 61.87 and 80,25 mg/mL. Mortality and changes in life cycle were recorded daily. Results: β-caryophyllene, β-selinene and eugenol, were the main constituents of the basil essential oil. The experiments demonstrated that in all concentrations tested, this essential oil shortening the duration of all post embryonic stages having a direct impact in the viability of this fly estimating the LC50 in 7.47 mg/mL of concentration. In addition, the essential oil caused morphological alterations in abdomen, wings and ptilinum at lower concentrations. Conclusions: This essential oil emerge as a good option for the control of the disease vector blowfly Chrysomya putoria.

  4. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

    Science.gov (United States)

    Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  5. Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

    Directory of Open Access Journals (Sweden)

    Razana Alwee

    2013-01-01

    Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

  6. VAR IPP-IPC Model Simulation

    Directory of Open Access Journals (Sweden)

    Juan P. Pérez Monsalve

    2014-12-01

    Full Text Available This work analyzed the relationship of the two main Price indicators in the Colombian economy, the IPP and the IPC. For this purpose, we identified the theory comprising both indexes to then develop a vector autoregressive model, which shows the reaction to shocks both in itself as in the other variable, whose impact continues propagating in the long term. Additionally, the work presents a simulation of the VAR model through the Monte Carlo method, verifying the coincidence in distributions of probability and volatility levels, as well as the existence correlation over time

  7. Presencia en Isla Fuerte, Bolívar de Lutzomyia evansi vector de leishmaniosis visceral Presense of lutzomyia evansi vector of visceral leishmaniosis in Isla Fuerte, Colombia

    Directory of Open Access Journals (Sweden)

    Ivan Darío Vélez

    1994-01-01

    Full Text Available Se hizo un Inventario de fauna flebotomínea en Isla Fuerte, Bolívar, costa caribe colombiana. Se demostró por primera vez en dicha Isla la presencia de Lu. evansi, Lu. gomezi y Lu. trinidadensis. La presencia de Lu. Evansi (91.7% de las 73 capturas, vector principal de la leishmaniosis visceral en la costa caribe colombiana, convierte a Isla Fuerte en una zona de riesgo potencial de transmisión de leishmaniosis visceral.

    An inventory was made of phiebotomidae fauna in Isla Fuerte, Bolívar, on the caribbean colombian coast. The presence of Lu. evansi was demonstrated for the first time in that island and this species constituted 91.7% of 73 captures. Therefore the island becomes a potential risk for transmission of visceralleish. maniosis since Lu. evansi its main vector.

  8. EFFECTS OF MONETARY POLICY IN ROMANIA - A VAR APPROACH

    Directory of Open Access Journals (Sweden)

    Iulian Popescu

    2012-10-01

    Full Text Available Understanding how monetary policy decisions affect inflation and other economic variables is particularly important. In this paper we consider the implications of monetary policy under the inflation targeting regime in Romania, based on an autoregressive vector method including recursive VAR and structural VAR (SVAR. Therefore, we focus on assessing the extent and persistence of monetary policy effects on gross domestic product (GDP, price level, extended monetary aggregate (M3 and exchange rate. The main results of VAR analysis reflect a negative response of consumer price index (CPI, GDP and M3 and positive nominal exchange rate behaviour to a monetary policy shock, and also a limited impact of a short-term interest rate shock in explaining the consumer prices, production and exchange rate fluctuations.

  9. Modeling Advertising Expenditures and Spillover Effects Applied to the U.S. Non-Alcoholic Beverage Industry: Vector Autoregression (VAR) and Polynomial Distributed Lag (PDL) Approaches

    OpenAIRE

    Dharmasena, Senarath; Capps, Oral, Jr.; Bessler, David A.

    2012-01-01

    The non-alcoholic beverage market in the U.S. is a multi-billion dollar industry growing steadily over the past decade. Also, non-alcoholic beverages are among the most heavily advertised food and beverage groups in the United States. Several studies pertaining to non-alcoholic beverages including the incorporation of advertising effects have been conducted, but most of these have centered attention on milk consumption. Some studies have considered demand interrelationships for several bevera...

  10. Inflows and their Macroeconomic Impact in India a VAR Analysis

    Directory of Open Access Journals (Sweden)

    Narayan Sethi

    2012-12-01

    Full Text Available The present study attempts to examine the effects of private foreign capital inflows (FINV on macroeconomic variables in India. The study also examines the trends and composition of capital inflows into India. Using the Vector Autoregression (VAR method, this paper specifically examines effects of private foreign capital inflows (FINV on macroeconomic variables in India. This study is based on the monthly data from 1995:04 to 2011:07 and incorporating the macroeconomic variables such as exchange rate (EXR, inflation, money supply (M3, export (EXPO, import (IMP, foreign exchange reserve (FOREX and economic growth (IIP as proxy of GDP. The important observations emerge from the VAR analysis which shows there is dynamic short and long equilibrium relationship between few macroeconomic variables like exchange rate (EXR, foreign exchange reserve (FOREX, index of industrial production (IIP and money supply (M3 with private foreign capital inflows (FINV during the study period from 1995:04 to 2011:07

  11. Integration of Financial Markets in Post Global Financial Crises and Implications for British Financial Sector: Analysis Based on A Panel VAR Model

    OpenAIRE

    Nasir, M; Du, M

    2017-01-01

    This study analyses the dynamics of integration among global financial markets in the context of Global Financial Crisis (2008) by employing a Panel Vector Autoregressive (VAR) model on the monthly data of nine countries and three markets from Jan 2003 to Oct 2015. It was found that there has been a shift in the association among the global financial markets since Global Financial Crisis (GFC).Moreover, the British financial sectors in Post-GFC world clearly showed a change in the association...

  12. Generalizing smooth transition autoregressions

    DEFF Research Database (Denmark)

    Chini, Emilio Zanetti

    We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with part......We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail......, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling...

  13. Pemodelan Markov Switching Autoregressive

    OpenAIRE

    Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi

    2014-01-01

    Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, tran...

  14. Bootstrap Determination of the Co-Integration Rank in Heteroskedastic VAR Models

    DEFF Research Database (Denmark)

    Cavaliere, G.; Rahbek, Anders; Taylor, A.M.R.

    2014-01-01

    In a recent paper Cavaliere et al. (2012) develop bootstrap implementations of the (pseudo-) likelihood ratio (PLR) co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates...... of the underlying vector autoregressive (VAR) model which obtain under the reduced rank null hypothesis. They propose methods based on an independent and individual distributed (i.i.d.) bootstrap resampling scheme and establish the validity of their proposed bootstrap procedures in the context of a co......-integrated VAR model with i.i.d. innovations. In this paper we investigate the properties of their bootstrap procedures, together with analogous procedures based on a wild bootstrap resampling scheme, when time-varying behavior is present in either the conditional or unconditional variance of the innovations. We...

  15. Estimation bias and bias correction in reduced rank autoregressions

    DEFF Research Database (Denmark)

    Nielsen, Heino Bohn

    2017-01-01

    This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...

  16. An extension of cointegration to fractional autoregressive processes

    DEFF Research Database (Denmark)

    Johansen, Søren

    This paper contains an overview of some recent results on the statistical analysis of cofractional processes, see Johansen and Nielsen (2010). We first give an brief summary of the analysis of cointegration in the vector autoregressive model and then show how this can be extended to fractional pr...

  17. Evaluating Liquid and Granular Bacillus thuringiensis var. israelensis Broadcast Applications for Controlling Vectors of Dengue and Chikungunya Viruses in Artificial Containers and Tree Holes.

    Science.gov (United States)

    Harwood, James F; Farooq, Muhammad; Turnwall, Brent T; Richardson, Alec G

    2015-07-01

    The principal vectors of chikungunya and dengue viruses typically oviposit in water-filled artificial and natural containers, including tree holes. Despite the risk these and similar tree hole-inhabiting mosquitoes present to global public health, surprisingly few studies have been conducted to determine an efficient method of applying larvicides specifically to tree holes. The Stihl SR 450, a backpack sprayer commonly utilized during military and civilian vector control operations, may be suitable for controlling larval tree-hole mosquitoes, as it is capable of delivering broadcast applications of granular and liquid dispersible formulations of Bacillus thuringiensis var. israelensis (Bti) to a large area relatively quickly. We compared the application effectiveness of two granular (AllPro Sustain MGB and VectoBac GR) and two liquid (Aquabac XT and VectoBac WDG) formulations of Bti in containers placed on bare ground, placed beneath vegetative cover, and hung 1.5 or 3 m above the ground to simulate tree holes. Aedes aegypti (L.) larval mortality and Bti droplet and granule density data (when appropriate) were recorded for each formulation. Overall, granular formulations of Bti resulted in higher mortality rates in the simulated tree-hole habitats, whereas applications of granular and liquid formulations resulted in similar levels of larval mortality in containers placed on the ground in the open and beneath vegetation. Published by Oxford University Press on behalf of Entomological Society of America 2015. This work is written by US Government employees and is in the public domain in the US.

  18. Analysis of Budget Deficits and Macroeconomic Fundamentals: A VAR-VECM Approach

    Directory of Open Access Journals (Sweden)

    Manamba Epaphra

    2017-10-01

    Full Text Available Aim/purpose - This paper examines the relationship between budget deficits and selected macroeconomic variables in Tanzania for the period spanning from 1966 to 2015. Design/methodology/approach - The paper uses Vector autoregression (VAR - Vector Error Correction Model (VECM and variance decomposition techniques. The Johansen's test is applied to examine the long run relationship among the variables under study. Findings - The Johansen's test of cointegration indicates that the variables are cointegrated and thus have a long run relationship. The results based on the VAR-VECM estimation show that real GDP and exchange rate have a negative and significant relationship with budget deficit whereas inflation, money supply and lending interest rate have a positive one. Variance decomposition results show that variances in the budget deficits are mostly explained by the real GDP, followed by inflation and real exchange rate. Research implications/limitations - Results are very indicative, but highlight the importance of containing inflation and money supply to check their effects on budget deficits over the short run and long-run periods. Also, policy recommendation calls for fiscal authorities in Tanzania to adopt efficient and effective methods of tax collection and public sector spending. Originality/value/contribution - Tanzania has been experiencing budget deficit since the 1970s and that this budget deficit has been blamed for high indebtedness, inflation and poor investment and growth. The paper contributes to the empirical debate on the causal relationship between budget deficits and macroeconomic variables by employing VAR-VECM and variance decomposition approaches.

  19. Vectors

    DEFF Research Database (Denmark)

    Boeriis, Morten; van Leeuwen, Theo

    2017-01-01

    should be taken into account in discussing ‘reactions’, which Kress and van Leeuwen link only to eyeline vectors. Finally, the question can be raised as to whether actions are always realized by vectors. Drawing on a re-reading of Rudolf Arnheim’s account of vectors, these issues are outlined......This article revisits the concept of vectors, which, in Kress and van Leeuwen’s Reading Images (2006), plays a crucial role in distinguishing between ‘narrative’, action-oriented processes and ‘conceptual’, state-oriented processes. The use of this concept in image analysis has usually focused...

  20. THE DETERMINANTS OF STOCK MARKET INDEX: VAR APPROACH TO TURKISH STOCK MARKET

    Directory of Open Access Journals (Sweden)

    Eşref Savaş Başçı

    2013-01-01

    Full Text Available In this paper, we examined the relationship between ISE 100 Index and a set of four macroeconomic variables using Vector Autoregressive (VAR model. Variables we used in our model are Exchange, Gold, Import, Export and ISE 100 Index. ISE 100 Index is a dependent variable and the others are independent variables. In this study we used 190 observations for the sample period from January, 1996 to October, 2011. All variables have seasonal movements. After seasonal adjustments, all series have had stationary in their first difference. After determining optimal lag order, it was given one standard deviation shock for each series and their response. And in variance decomposition carried out subsequently, it has been determined that especially as of the second default of exchange, it was explained 31% by share indices.

  1. A comparison of the VAR model and the PC factor model in forecasting inflation in Montenegro

    Directory of Open Access Journals (Sweden)

    Lipovina-Božović Milena

    2013-01-01

    Full Text Available Montenegro started using the euro in 2002 and regained independence in 2006. Its main economic partners are European countries, yet inflation movements in Montenegro do not coincide with consumer price fluctuations in the eurozone. Trying to develop a useful forecasting model for Montenegrin inflation, we compare the results of a three-variable vector autoregression (VAR model, and a principle component (PC factor model starting with twelve variables. The estimation period is January 2001 to December 2012, and the control months are the first six months of 2013. The results show that in forecasting inflation, despite a high level of Montenegrin economic dependence on international developments, more reliable forecasts are achieved with the use of additional information on a larger number of factors, which includes domestic economic activity.

  2. International Trade, Pollution Accumulation and Sustainable Growth: A VAR Estimation from the Pearl River Delta Region

    Science.gov (United States)

    Zuo, Hui; Tian, Lu

    2018-03-01

    In order to investigate international trade influence in the regional environment. This paper constructs a vector auto-regression (VAR) model and estimates the equations with the environment and trade data of the Pearl River Delta Region. The major mechanisms to the lag are discussed and the fit simulation of the environmental change by the international impulse is given. The result shows that impulse of pollution-intensive export deteriorates the environment continuously and impulse of such import improves it. These effects on the environment are insignificantly correlated with contemporary regional income but significantly correlative to early-stage trade feature. To a typical trade-dependent economy, both export and import have hysteresis influence in the regional environment. The lagged impulse will change environmental development in the turning point, maximal pollution level and convergence.

  3. Forecasting daily political opinion polls using the fractionally cointegrated VAR model

    DEFF Research Database (Denmark)

    Nielsen, Morten Ørregaard; Shibaev, Sergei S.

    We examine forecasting performance of the recent fractionally cointegrated vector autoregressive (FCVAR) model. We use daily polling data of political support in the United Kingdom for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four...... trend from the model follows the vote share of the UKIP very closely, and we thus interpret it as a measure of Euro-skepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated...... variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated VAR (CVAR) model at all forecast horizons. The relative forecast improvement...

  4. Texture classification using autoregressive filtering

    Science.gov (United States)

    Lawton, W. M.; Lee, M.

    1984-01-01

    A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

  5. Autoregressive Moving Average Graph Filtering

    OpenAIRE

    Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert

    2016-01-01

    One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...

  6. IDENTIFYING BANK LENDING CHANNEL IN INDONESIA: A VECTOR ERROR CORRECTION APPROACH WITH STRUCTURAL BREAK

    Directory of Open Access Journals (Sweden)

    Akhsyim Afandi

    2017-03-01

    Full Text Available There was a question whether monetary policy works through bank lending channelrequired a monetary-induced change in bank loans originates from the supply side. Mostempirical studies that employed vector autoregressive (VAR models failed to fulfill thisrequirement. Aiming to offer a solution to this identification problem, this paper developed afive-variable vector error correction (VEC model of two separate bank credit markets inIndonesia. Departing from previous studies, the model of each market took account of onestructural break endogenously determined by implementing a unit root test. A cointegrationtest that took account of one structural break suggested two cointegrating vectors identifiedas bank lending supply and demand relations. The estimated VEC system for both marketssuggested that bank loans adjusted more strongly in the direction of the supply equation.

  7. Kumaraswamy autoregressive moving average models for double bounded environmental data

    Science.gov (United States)

    Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

    2017-12-01

    In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

  8. Behavioural Pattern of Causality Parameter of Autoregressive ...

    African Journals Online (AJOL)

    In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...

  9. Optimal transformations for categorical autoregressive time series

    NARCIS (Netherlands)

    Buuren, S. van

    1996-01-01

    This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze

  10. Oil price fluctuations and their impact on the macroeconomic variables of Kuwait: a case study using a VAR model

    International Nuclear Information System (INIS)

    Eltony, M. Nagy; Al-Awadi, Mohammad

    2001-01-01

    In this study, a vector autoregression model (VAR) and a vector error correction model (VECM) were estimated to examine the impact of oil price fluctuations on seven key macroeconomic variables for the Kuwaiti economy. Quarterly data for the period 1984-1998 were utilised. Theoretically and empirically speaking, VECM is superior to the VAR approach. Also, the results corresponding to the VECM model are closer to common sense. However, the estimated models indicate a high degree of interrelation between major macroeconomic variables. The empirical results highlight the causality running from the oil prices and oil revenues, to government development and current expenditure and then towards other variables. For the most part, the empirical evidence indicates that oil price shocks and hence oil revenues have a notable impact on government expenditure, both development and current. However, government development expenditure has been influenced relatively more. The results also point out the significant of the CPI in explaining a notable part of the variations of both types of government expenditure. On the other hand, the variations in value of imports are mostly accounted for by oil revenue fluctuations. On the other hand, the variations in value of imports are mostly accounted for by oil revenue fluctuations and then by the fluctuation in government development expenditures. Also, the results from the VECM approach indicate that a significant part of LM2 variance is explained by the variance in oil revenue. It reaches about 46 per cent in the 10th quarter, even more than its own variations. (Author)

  11. Identification of Civil Engineering Structures using Vector ARMA Models

    DEFF Research Database (Denmark)

    Andersen, P.

    The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....

  12. THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...

    African Journals Online (AJOL)

    The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.

  13. A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Hu, Jianming

    2015-01-01

    With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.

  14. Modeling vector nonlinear time series using POLYMARS

    NARCIS (Netherlands)

    de Gooijer, J.G.; Ray, B.K.

    2003-01-01

    A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

  15. Temporal Aggregation in First Order Cointegrated Vector Autoregressive models

    DEFF Research Database (Denmark)

    Milhøj, Anders; la Cour, Lisbeth Funding

    2011-01-01

    with the frequency of the data. We also introduce a graphical representation that will prove useful as an additional informational tool for deciding the appropriate cointegration rank of a model. In two examples based on models of time series of different grades of gasoline, we demonstrate the usefulness of our...

  16. Forecasting Hong Kong economy using factor augmented vector autoregression

    OpenAIRE

    Pang, Iris Ai Jao

    2010-01-01

    This work applies the FAVAR model to forecast GDP growth rate, unemployment rate and inflation rate of the Hong Kong economy. There is no factor model forecasting literature on the Hong Kong economy. The objective is to find out whether factor forecasting of using a large dataset can improve forecast performance of the Hong Kong economy. To avoid misspecification of the number of factors in the FAVAR, combination forecasts are constructed. It is found that forecasts from FAVAR model overall o...

  17. Unit root vector autoregression with volatility induced stationarity

    DEFF Research Database (Denmark)

    Rahbek, Anders; Nielsen, Heino Bohn

    We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...

  18. Unit Root Vector Autoregression with volatility Induced Stationarity

    DEFF Research Database (Denmark)

    Rahbek, Anders; Nielsen, Heino Bohn

    We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...

  19. Identification and estimation of non-Gaussian structural vector autoregressions

    DEFF Research Database (Denmark)

    Lanne, Markku; Meitz, Mika; Saikkonen, Pentti

    -Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....

  20. Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    A. Alexandre Trindade

    2003-02-01

    Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.

  1. Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors

    NARCIS (Netherlands)

    Hecq, Alain; Issler, J.V.; Telg, Sean

    2017-01-01

    The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into

  2. Interval Forecast for Smooth Transition Autoregressive Model ...

    African Journals Online (AJOL)

    In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...

  3. New interval forecast for stationary autoregressive models ...

    African Journals Online (AJOL)

    In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...

  4. Forecasting nuclear power supply with Bayesian autoregression

    International Nuclear Information System (INIS)

    Beck, R.; Solow, J.L.

    1994-01-01

    We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)

  5. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  6. Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

    International Nuclear Information System (INIS)

    Garcia-Ascanio, Carolina; Mate, Carlos

    2010-01-01

    Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

  7. The asymmetric effects of oil price and monetary policy shocks. A nonlinear VAR approach

    International Nuclear Information System (INIS)

    Rahman, Sajjadur; Serletis, Apostolos

    2010-01-01

    In this paper we investigate the asymmetric effects of oil price shocks and monetary policy on macroeconomic activity, using monthly data for the United States, over the period from 1983:1 to 2008:12. In doing so, we use a logistic smooth transition vector autoregression (VAR), as detailed in Terasvirta and Anderson (1992) and Weise (1999), and make a distinction between two oil price volatility regimes (high and low), using the realized oil price volatility as a switching variable. We isolate the effects of oil price and monetary policy shocks and their asymmetry on output growth and, following Koop et al. (1996) and Weise (1999), we employ simulation methods to calculate Generalized Impulse Response Functions (GIRFs) to trace the effects of independent shocks on the conditional means of the variables. Our results suggest that in addition to the price of oil, oil price volatility has an impact on macroeconomic activity and that monetary policy is not only reinforcing the effects of oil price shocks on output, it is also contributing to the asymmetric response of output to oil price shocks. (author)

  8. The asymmetric effects of oil price and monetary policy shocks. A nonlinear VAR approach

    Energy Technology Data Exchange (ETDEWEB)

    Rahman, Sajjadur [Department of Economics, University of Saskatchewan, Saskatoon (Canada); Serletis, Apostolos [Department of Economics, University of Calgary, Calgary (Canada)

    2010-11-15

    In this paper we investigate the asymmetric effects of oil price shocks and monetary policy on macroeconomic activity, using monthly data for the United States, over the period from 1983:1 to 2008:12. In doing so, we use a logistic smooth transition vector autoregression (VAR), as detailed in Terasvirta and Anderson (1992) and Weise (1999), and make a distinction between two oil price volatility regimes (high and low), using the realized oil price volatility as a switching variable. We isolate the effects of oil price and monetary policy shocks and their asymmetry on output growth and, following Koop et al. (1996) and Weise (1999), we employ simulation methods to calculate Generalized Impulse Response Functions (GIRFs) to trace the effects of independent shocks on the conditional means of the variables. Our results suggest that in addition to the price of oil, oil price volatility has an impact on macroeconomic activity and that monetary policy is not only reinforcing the effects of oil price shocks on output, it is also contributing to the asymmetric response of output to oil price shocks. (author)

  9. Analysis of Influence Factors of PM2.5 in Chengdu Based on VAR Model

    Science.gov (United States)

    Mingzhi, Luo

    2017-05-01

    Air pollution and smog are the serious harms to public health and has attracted public attention. Based on the vector auto-regressive (VAR) model, we analysed the influence factors of PM2.5 in Chengdu, investigated the effect of other kinds of air pollutants and meteorological factors onthe PM2.5 by using the methods of generalized impulse response function, variance decomposition analysis, Granger causality test and therelated daily data from December 1, 2013 to November 14, 2016 in Chengdu city to the empirical study. The resultsshow that the influence factors of PM2.5 were stable;the increase of nitrogen dioxide, ozone,precipitation and temperature difference led to the increase of PM2.5 concentration while the increase ofthe wind speed, PM10, sulphur dioxide and carbon monoxide resulted in the decrease of PM2.5 concentration.Climate conditions,nitrogen dioxide and ozone are Granger causes for PM2.5.It is suggestedthat the key for the control of PM2.5 must be based on the cause and formation rules of PM2.5.A further study on nitrogen dioxide and ozone may play an important role in finding out the real source and formation reasons of PM2.5.

  10. Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

    Science.gov (United States)

    Zhang, Chi; Zhou, Kaile; Yang, Shanlin; Shao, Zhen

    2017-05-01

    Since the reforming and opening up in 1978, China has experienced a miraculous development. To investigate the transformation and upgrading of China's economy, this study focuses on the relationship between economic growth and electricity consumption of the secondary and tertiary industry in China. This paper captures the dynamic interdependencies among the related variables using a theoretical framework based on a Vector Autoregressive (VAR)-Vector Error Correction (VEC) model. Using the macroeconomic and electricity consumption data, the results show that, for secondary industry, there is only a unidirectional Granger causality from electricity consumption to Gross Domestic Product (GDP) from 1980 to 2000. However, for the tertiary industry, it only occurs that GDP Granger causes electricity consumption from 2001 to 2014. All these conclusions are verified by the impulse response function and variance decomposition. This study has a great significance to reveal the relationship between industrial electricity consumption and the pattern of economic development. Meanwhile, it further suggests that, since China joined the World Trade Organization (WTO) in 2001, the trend of the economic transformation and upgrading has gradually appeared.

  11. Multistage Stochastic Programming via Autoregressive Sequences

    Czech Academy of Sciences Publication Activity Database

    Kaňková, Vlasta

    2007-01-01

    Roč. 15, č. 4 (2007), s. 99-110 ISSN 0572-3043 R&D Projects: GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : Economic proceses * Multistage stochastic programming * autoregressive sequences * individual probability constraints Subject RIV: BB - Applied Statistics, Operational Research

  12. On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

    Science.gov (United States)

    Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

    2017-06-01

    Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

  13. Presencia de vectores de leishmaniosis cutánea y visceral en la Isla de Santa Cruz de Mompox, Departamento de Bolívar, Colombia Vectors of cutaneous and visceral Leishmaniosis in San Cruz de Mompox island, Bolívar, Colombia

    Directory of Open Access Journals (Sweden)

    Iván Darío Vélez Bernal

    1994-04-01

    Full Text Available

    Se realizó una prospección entomológica en la Isla de Santa Cruz de Mompox entre los días 23 y 29 de diciembre de 1993, con el fin de determinar si se trata de una zona de riesgo potencial de Infección por Leishmania; para ello se practicaron capturas de flebotomíneos en sitios de reposo diurno y con trampas de luz tipo CDC y Shannon, colocadas simultáneamente en el perl y el extradomicilio. Se colectaron 367 ejemplares de flebotomíneos cuya Identificación demostró por primera vez la presencia en la Isla de siete especies de Lutzomyia entre las cuales se encuentran Lu. panamensis y Lu. gomezi, vectoras de leishmaniosis cutánea y Lu. evansi vectora principal de la forma visceral en la Costa Caribe Colombiana; el hallazgo demuestra que la isla es una zona de riesgo potencial para leishmaniosis cutánea y visceral.

    An entomological survey was carried out at the island of Santa Cruz of Mompox between December 23 and 29, 1993, In order to determine if there is a potential risk for Leishmania infection. Phlebotominae were captured at sites of diurnal rest as well as using CDC and Shannon light traps, simultaneously located at extra and peridomiciliary sites. 367 specimens were collected, among them 7 species of Lutzomyia including Lu. panamensis and Lu. gomezi vectors of cutaneous leishmaniosis; also Lu. evansi, the main vector of visceral leishmaniosis In the Colombian Caribbean Coast. This is the first report implicating this island as a potential risk site for cutaneous and visceral leishmaniosis.

  14. Chain binomial models and binomial autoregressive processes.

    Science.gov (United States)

    Weiss, Christian H; Pollett, Philip K

    2012-09-01

    We establish a connection between a class of chain-binomial models of use in ecology and epidemiology and binomial autoregressive (AR) processes. New results are obtained for the latter, including expressions for the lag-conditional distribution and related quantities. We focus on two types of chain-binomial model, extinction-colonization and colonization-extinction models, and present two approaches to parameter estimation. The asymptotic distributions of the resulting estimators are studied, as well as their finite-sample performance, and we give an application to real data. A connection is made with standard AR models, which also has implications for parameter estimation. © 2011, The International Biometric Society.

  15. on the performance of Autoregressive Moving Average Polynomial

    African Journals Online (AJOL)

    Timothy Ademakinwa

    Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag ... Moving Average Polynomial Distributed Lag (ARMAPDL) model. ..... Global Journal of Mathematics and Statistics. Vol. 1. ... Business and Economic Research Center.

  16. Kepler AutoRegressive Planet Search (KARPS)

    Science.gov (United States)

    Caceres, Gabriel

    2018-01-01

    One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

  17. dbVar

    Data.gov (United States)

    U.S. Department of Health & Human Services — dbVar is a database of genomic structural variation. It accepts data from all species and includes clinical data. It can accept diverse types of events, including...

  18. On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility

    International Nuclear Information System (INIS)

    Jebabli, Ikram; Arouri, Mohamed; Teulon, Frédéric

    2014-01-01

    Transmission of price shocks from one market to another one has long been investigated in the economic literature. However, studies have namely dealt with the relationship between financial and energy markets. With the recent changes in market conditions, investors, policy-makers and interest groups are giving special attention to food market. This paper aims at analyzing shock transmission between international food, energy and financial markets and to provide some insights into the volatility behavior during the past years and discuss its implications for portfolio management. To do this, we present a new time varying parameter VAR (TVP-VAR) model with stochastic volatility approach which provides extreme flexibility with a parsimonious specification. We resort also to a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering for the assessment of total and directional volatility spillovers. Our main findings suggest that volatility spillovers increase considerably during crisis and, namely after mid-2008, when stock markets become net transmitter of volatility shocks while crude oil becomes a net receiver. Shocks to crude oil or MSCI markets have immediate and short-term impacts on food markets which are emphasized during the financial crisis period. Moreover, we show that augmenting a diversified portfolio of food commodities with crude oil or stocks significantly increases its risk-adjusted performance. - Highlights: • We study shock transmission between food, energy and financial markets. • We use a new time-varying parameter VAR model with stochastic volatility. • There is volatility spillover from oil and stock markets to food. • Volatility spillovers increase considerably during crisis, namely after mid-2008. • Augmenting a portfolio of foods with oil or stocks increases its performance

  19. Kepler AutoRegressive Planet Search

    Science.gov (United States)

    Caceres, Gabriel Antonio; Feigelson, Eric

    2016-01-01

    The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real

  20. Incorporating measurement error in n=1 psychological autoregressive modeling

    NARCIS (Netherlands)

    Schuurman, Noemi K.; Houtveen, Jan H.; Hamaker, Ellen L.

    2015-01-01

    Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive

  1. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  2. Hvem var morderen?

    DEFF Research Database (Denmark)

    Andersen, Lene Vinther

    2014-01-01

    Folkemindesamleren og forfatteren Helene Strange (1874-1943) skrev flere gange om et giftmord på en præst på Nordfalster i 1755. Hun satte spørgsmålstegn ved højesteretsdommen og hævdede, at en uskyldig pige blev dømt, mens den virkelige morder slap fri. Det var folkets uskrevne dom, som i over...... hundrede år var blevet fortalt mundtligt videre på egnen, og hun forsøgte – med forskellige midler – at få bragt denne version af historien frem i offentligheden....

  3. Paulus var kristendommens Lenin

    DEFF Research Database (Denmark)

    Thyssen, Ole

    2017-01-01

    En af Luthers inspirationskilder var Paulus, hvis løsning på lokale problemer fik globale konsekvenser. Han trak en skarp grænse mellem det snavsede kød og den rene sjæl og mellem vantro og tro.......En af Luthers inspirationskilder var Paulus, hvis løsning på lokale problemer fik globale konsekvenser. Han trak en skarp grænse mellem det snavsede kød og den rene sjæl og mellem vantro og tro....

  4. Penalised Complexity Priors for Stationary Autoregressive Processes

    KAUST Repository

    Sø rbye, Sigrunn Holbek; Rue, Haavard

    2017-01-01

    The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.

  5. Penalised Complexity Priors for Stationary Autoregressive Processes

    KAUST Repository

    Sørbye, Sigrunn Holbek

    2017-05-25

    The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users\\' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.

  6. On robust forecasting of autoregressive time series under censoring

    OpenAIRE

    Kharin, Y.; Badziahin, I.

    2009-01-01

    Problems of robust statistical forecasting are considered for autoregressive time series observed under distortions generated by interval censoring. Three types of robust forecasting statistics are developed; meansquare risk is evaluated for the developed forecasting statistics. Numerical results are given.

  7. Information contraction and extraction by multivariate autoregressive (MAR) modelling. Pt. 2. Dominant noise sources in BWRS

    International Nuclear Information System (INIS)

    Morishima, N.

    1996-01-01

    The multivariate autoregressive (MAR) modeling of a vector noise process is discussed in terms of the estimation of dominant noise sources in BWRs. The discussion is based on a physical approach: a transfer function model on BWR core dynamics is utilized in developing a noise model; a set of input-output relations between three system variables and twelve different noise sources is obtained. By the least-square fitting of a theoretical PSD on neutron noise to an experimental one, four kinds of dominant noise sources are selected. It is shown that some of dominant noise sources consist of two or more different noise sources and have the spectral properties of being coloured and correlated with each other. By diagonalizing the PSD matrix for dominant noise sources, we may obtain an MAR expression for a vector noise process as a response to the diagonal elements(i.e. residual noises) being white and mutually-independent. (Author)

  8. Incorporating measurement error in n = 1 psychological autoregressive modeling

    Science.gov (United States)

    Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.

    2015-01-01

    Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988

  9. The Relevance of Political Stability on FDI: A VAR Analysis and ARDL Models for Selected Small, Developed, and Instability Threatened Economies

    Directory of Open Access Journals (Sweden)

    Petar Kurecic

    2017-06-01

    Full Text Available This paper studies the relevance of political stability on foreign direct investment (FDI and the relevance of FDI on economic growth, in three panels. The first panel contains 11 very small economies; the second contains five well-developed and politically stable economies with highly positive FDI net inflows, while the third is a panel with economies that are prone to political violence or targeted by the terrorist attacks. We employ a Granger causality test and implement a vector autoregressive (VAR framework within the panel setting. In order to test the sensitivity of the results and avoid robust errors, we employ an ARDL model for each of the countries within every panel. Based upon our results, we conclude that there is a long-term relationship between political stability and FDI for the panel of small economies, while we find no empiric evidence of such a relationship for both panels of larger and more developed economies. Similarly to the original hypothesis of Lucas (1990, we find that FDI outflows tend to go towards politically less stable countries. On the other hand, the empiric methodology employed did not find such conclusive evidence in the panels of politically more developed countries or in the small economies that this paper observes.

  10. A complex autoregressive model and application to monthly temperature forecasts

    Directory of Open Access Journals (Sweden)

    X. Gu

    2005-11-01

    Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.

  11. Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

    Directory of Open Access Journals (Sweden)

    W. Wang

    2005-01-01

    Full Text Available Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average models for seasonal streamflow series. However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity effect, a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.

  12. Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

    Directory of Open Access Journals (Sweden)

    Isao Ishida

    2015-01-01

    Full Text Available We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500 and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.

  13. Application of autoregressive moving average model in reactor noise analysis

    International Nuclear Information System (INIS)

    Tran Dinh Tri

    1993-01-01

    The application of an autoregressive (AR) model to estimating noise measurements has achieved many successes in reactor noise analysis in the last ten years. The physical processes that take place in the nuclear reactor, however, are described by an autoregressive moving average (ARMA) model rather than by an AR model. Consequently more correct results could be obtained by applying the ARMA model instead of the AR model to reactor noise analysis. In this paper the system of the generalised Yule-Walker equations is derived from the equation of an ARMA model, then a method for its solution is given. Numerical results show the applications of the method proposed. (author)

  14. A note on intrinsic conditional autoregressive models for disconnected graphs

    KAUST Repository

    Freni-Sterrantino, Anna; Ventrucci, Massimo; Rue, Haavard

    2018-01-01

    In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.

  15. Robust bayesian analysis of an autoregressive model with ...

    African Journals Online (AJOL)

    In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...

  16. Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)

    DEFF Research Database (Denmark)

    Agosto, Arianna; Cavaliere, Guiseppe; Kristensen, Dennis

    We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn used...

  17. A note on intrinsic conditional autoregressive models for disconnected graphs

    KAUST Repository

    Freni-Sterrantino, Anna

    2018-05-23

    In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.

  18. Stable Parameter Estimation for Autoregressive Equations with Random Coefficients

    Directory of Open Access Journals (Sweden)

    V. B. Goryainov

    2014-01-01

    Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross

  19. Da Danmark var en slavenation

    DEFF Research Database (Denmark)

    Peters, Rikke Louise Alberg; Brunbech, Peter Johan Yding; Poulsen, Jens Aage

    I mere end 250 år var Danmark en stor kolonimagt. Skt. Jan, Skt. Thomas og Skt. Croix, eller U.S. Virgin Islands, som de kaldes i dag, var danske kolonier, hvor der bl.a. blev dyrket sukkerrør og bomuld. Det hårde arbejde blev udført af slaver, som man fra slutningen af 1600-tallet begyndte at tr...

  20. Likelihood inference for a nonstationary fractional autoregressive model

    DEFF Research Database (Denmark)

    Johansen, Søren; Ørregård Nielsen, Morten

    2010-01-01

    This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...

  1. Mathematical model with autoregressive process for electrocardiogram signals

    Science.gov (United States)

    Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de

    2018-04-01

    The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.

  2. On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

    Science.gov (United States)

    1976-08-01

    a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P

  3. CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Hansen, Lars Kai

    2004-01-01

    We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

  4. var. puiggarianum (Batrachospermales, Rhodophyta

    Directory of Open Access Journals (Sweden)

    María Cecilia Gauna

    2005-01-01

    Full Text Available Se coleccionó Batrachospermum atrum var. puiggarianum por primera vez en la provincia de Buenos Aires. La identificación de las muestras se basó en el análisis de la morfología microscópica y en el número cromosómico de cada una de las generaciones de su ciclo de vida bajo cultivo. Los talos se estudiaron con microscopio óptico, y la cariología, por medio de la técnica de carmín acético. El ciclo de vida presentó tres generaciones: una gametofítica haploide, una carposporófitica diploide que originó la última fase Chantransia diploide. Los talos gametófitos estuvieron formados por verticilos separados por zonas internodales, cada uno de ellos constituidos por ramas primarias densamente comprimidas. Entre éstas se observaron ramas portadoras de espermatangios y de carpogonios. Las zonas internodales estuvieron constituidas por células corticales y axiales. Los carposporófitos ovoideos estuvieron formados por filamentos gonimoblásticos portadores de carposporangios terminales. El estado Chantransia se caracterizó por presentar filamentos cortos con pocas células. El material estudiado presentó un número haploide n = 4 y diploide 2n = 8.

  5. Identification of BWR feedwater control system using autoregressive integrated model

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Andoh, Yasumasa; Yamamoto, Fumiaki; Idesawa, Masato; Itoh, Kazuo.

    1983-01-01

    With the view of contributing toward more reliable interpretation of noise behavior under normal operating conditions, which is essential for correct detection and/or diagnosis of incipient anomalies in nuclear power plants by noise analysis technique, studies has been undertaken of the noise behavior in a BWR feedwater control system, with use made of a multivariate autoregressive modeling technique. Noise propagation mechanisms as well as open- and closed-loop responses in the system are identified from noise data by a method in which an autoregressive integrated model is introduced. The closed-loop responses obtained with this method are compared with transient data from an actual test, and confirmed to be reliable in estimating semi-quantitative features. Other analyses performed with this model also yield results that appear most reasonable in their physical characteristics. These results have demonstrated the effectiveness of the noise analyses technique based on the autoregressive integrated model for evaluating and diagnosing the performance of feedwater control systems. (author)

  6. A fractionally cointegrated VAR analysis of economic voting and political support

    DEFF Research Database (Denmark)

    Jones, Maggie E. C.; Ørregård Nielsen, Morten; Popiel, Michael Ksawery

    We use a fractionally cointegrated vector autoregressive model to examine the relationship between Canadian political support and macroeconomic conditions. This model is well suited for the analysis because it allows multiple fractional time series and admits simple asymptotic inference for the m......We use a fractionally cointegrated vector autoregressive model to examine the relationship between Canadian political support and macroeconomic conditions. This model is well suited for the analysis because it allows multiple fractional time series and admits simple asymptotic inference...... for the model parameters and tests of the hypotheses of interest. In the long-run equilibrium, we find that support for the Progressive Conservative Party was higher during periods of high interest rates and low unemployment, while support for the Liberal Party was higher during periods of low interest rates...

  7. Formula I(1 and I(2: Race Tracks for Likelihood Maximization Algorithms of I(1 and I(2 Cointegrated VAR Models

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2017-11-01

    Full Text Available This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1 and I(2 models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research.

  8. Measurement of the CP-violation parameter Re(var-epsilon '/var-epsilon)

    International Nuclear Information System (INIS)

    Gibbons, L.K.; Barker, A.R.; Briere, R.A.; Makoff, G.; Papadimitriou, V.; Patterson, J.R.; Schwingenheuer, B.; Somalwar, S.V.; Wah, Y.W.; Winstein, B.; Winston, R.; Woods, M.; Yamamoto, H.; Swallow, E.C.; Bock, G.J.; Coleman, R.; Enagonio, J.; Hsiung, Y.B.; Ramberg, E.; Stanfield, K.; Tschirhart, R.; Yamanaka, T.; Gollin, G.D.; Karlsson, M.; Okamitsu, J.K.; Debu, P.; Peyaud, B.; Turlay, R.; Vallage, B.

    1993-01-01

    A measurement of the CP-violation parameter Re(var-epsilon '/var-epsilon) has been made using the full E731 data set. We find Re(var-epsilon '/var-epsilon)=(7.4±5.2±2.9)x10 -4 where the first error is statistical and the second systematic

  9. Methodology for the AutoRegressive Planet Search (ARPS) Project

    Science.gov (United States)

    Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration

    2018-01-01

    The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.

  10. Autoregressive Processes in Homogenization of GNSS Tropospheric Data

    Science.gov (United States)

    Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.

    2016-12-01

    Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.

  11. Bias-corrected estimation in potentially mildly explosive autoregressive models

    DEFF Research Database (Denmark)

    Haufmann, Hendrik; Kruse, Robinson

    This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...

  12. Analysis of nonlinear systems using ARMA [autoregressive moving average] models

    International Nuclear Information System (INIS)

    Hunter, N.F. Jr.

    1990-01-01

    While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs

  13. Testing the Conditional Mean Function of Autoregressive Conditional Duration Models

    DEFF Research Database (Denmark)

    Hautsch, Nikolaus

    be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor......This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors...

  14. Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications

    DEFF Research Database (Denmark)

    Teräsvirta, Timo; Yang, Yukai

    is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists...

  15. Testing for co-integration in vector autoregressions with non-stationary volatility

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.

    2010-01-01

    cases. We show that the conventional rank statistics computed as in (Johansen, 1988) and (Johansen, 1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size...... and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, or to assume...

  16. Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression

    DEFF Research Database (Denmark)

    Lanne, Markku; Luoto, Jani

    a genuinely noninformative prior and thus learning from the data about the impulse responses. While the shocks are statistically identified, they carry no economic meaning as such, and we propose a procedure for labeling them by their probabilities of satisfying each of the given sign restrictions...

  17. Valuing structure, model uncertainty and model averaging in vector autoregressive processes

    NARCIS (Netherlands)

    R.W. Strachan (Rodney); H.K. van Dijk (Herman)

    2004-01-01

    textabstractEconomic policy decisions are often informed by empirical analysis based on accurate econometric modeling. However, a decision-maker is usually only interested in good estimates of outcomes, while an analyst must also be interested in estimating the model. Accurate inference on

  18. How Rational Are Inflation Expectations? A Vector Autoregression Decomposition of Inflation Forecasts and Their Errors

    National Research Council Canada - National Science Library

    Ladvogt, Timothy

    2002-01-01

    ... inflation forecast data from the Livingston Survey and the ASA/NBER Survey of Professional Forecasters over the past 30 years to determine what publicly available macroeconomic information, if any, explains...

  19. Testing for rational bubbles in a co-explosive vector autoregression

    DEFF Research Database (Denmark)

    Engsted, Tom; Nielsen, Bent

    , are derived both for a model without bubbles and for a model with a rational bubble. In both cases we show how the restrictions can be tested through standard chi-squared inference. The analysis for the no-bubble case is done within the traditional Johansen model for I(1) variables, while the bubble model...

  20. Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression

    DEFF Research Database (Denmark)

    Bork, Lasse

    ) by Bernanke et al. (2005). I estimate the FAVAR by the fully parametric one-step EM algorithm as an alternative to the two-step principal component method and the one-step Bayesian method in Bernanke et al. (2005). The EM algorithm which is an iterative maximum likelihood method estimates all the parameters...

  1. Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy

    DEFF Research Database (Denmark)

    Callot, Laurent; Kristensen, Johannes Tang

    the monetary policy response to inflation and business cycle fluctuations in the US by estimating a parsimoniously time varying parameter Taylor rule.We document substantial changes in the policy response of the Fed in the 1970s and 1980s, and since 2007, but also document the stability of this response...

  2. DYNAMICS OF MUTUAL FUNDS IN RELATION TO STOCK MARKET: A VECTOR AUTOREGRESSIVE CAUSALITY ANALYSIS

    Directory of Open Access Journals (Sweden)

    Md. Shahadath Hossain

    2013-01-01

    Full Text Available In Bangladesh, primary and secondary mutual fund markets behave in a completely different way, where initial public offering (IPO investors of mutual funds earn more than 250 percent rerun, whereas secondary market investors cannot even manage to cover the opportunity cost of their investment. There are few other abnormalities present in this market – unlike everywhere in the world, most of the mutual funds are closed-end (92 percent and closed-end mutual funds are barred to issue bonus or right shares. A total of 714 day’s observations, from January 2008 to December 2010, of four variables– DSE (Dhaka Stock Exchange general index return, DSE general index turnover, mutual funds’ return and mutual funds’ turnover– are utilized. Stationarity of the variables are tested with Augmented Dickey-Fuller (ADF unit root test and found that variables are in different order of integration. Long-term equilibrium relationships among the variables are tested with Johansen cointegration and it is found that DSE general index return and mutual funds’ return are cointegrated. Toda-Yamamoto (TY version of granger non-causality test is employed and bidirectional causality is found moving from DSE (Dhaka Stock Exchange general index turnover to DSE general index return, whereas unidirectional causality is found moving from mutual fund’s return to DSE general index return, mutual funds’ return to mutual funds turnover, and DSE general index turnover to mutual funds turnover. This finding helps to conclude that equity shares’ demand drives the mutual funds demand but even higher demand of mutual funds fails to raise its own price unless underlying value of the mutual funds changes.

  3. Is the Proposed East African Monetary Union an Optimal Currency Area? A Structural Vector Autoregression Analysis

    OpenAIRE

    Steven K. Buigut; Neven Valev

    2004-01-01

    The treaty of 1999 to revive the defunct East African Community (EAC) ratified by Kenya, Uganda, and Tanzania came into force on July 2000 with the objective of fostering a closer co-operation in political, economic, social, and cultural fields. To achieve this, an East Africa Customs Union protocol was signed in March 2004. A Common Market, a Monetary Union, and ultimately a Political Federation of East Africa states is planned. Though the question of a monetary union has been discussed in t...

  4. Monthly streamflow forecasting with auto-regressive integrated moving average

    Science.gov (United States)

    Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

    2017-09-01

    Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

  5. Efficient transformation and expression of gfp gene in Valsa mali var. mali.

    Science.gov (United States)

    Chen, Liang; Sun, Gengwu; Wu, Shujing; Liu, Huixiang; Wang, Hongkai

    2015-01-01

    Valsa mali var. mali, the causal agent of valsa canker of apple, causes great loss of apple production in apple producing regions. The pathogenic mechanism of the pathogen has not been studied extensively, thus a suitable gene marker for pathogenic invasion analysis and a random insertion of T-DNA for mutants are desirable. In this paper, we reported the construction of a binary vector pKO1-HPH containing a positive selective gene hygromycin phosphotransferase (hph), a reporter gene gfp conferring green fluorescent protein, and an efficient protocol for V. mali var. mali transformation mediated by Agrobacterium tumefaciens. A transformation efficiency up to about 75 transformants per 10(5) conidia was achieved when co-cultivation of V. mali var. mali and A. tumefaciens for 48 h in A. tumefaciens inductive medium agar plates. The insertions of hph gene and gfp gene into V. mali var. mali genome verified by polymerase chain reaction and southern blot analysis showed that 10 randomly-selected transformants exhibited a single, unique hybridization pattern. This is the first report of A. tumefaciens-mediated transformation of V. mali var mali carrying a 'reporter' gfp gene that stably and efficiently expressed in the transformed V. mali var. mali species.

  6. Vector analysis

    CERN Document Server

    Newell, Homer E

    2006-01-01

    When employed with skill and understanding, vector analysis can be a practical and powerful tool. This text develops the algebra and calculus of vectors in a manner useful to physicists and engineers. Numerous exercises (with answers) not only provide practice in manipulation but also help establish students' physical and geometric intuition in regard to vectors and vector concepts.Part I, the basic portion of the text, consists of a thorough treatment of vector algebra and the vector calculus. Part II presents the illustrative matter, demonstrating applications to kinematics, mechanics, and e

  7. About vectors

    CERN Document Server

    Hoffmann, Banesh

    1975-01-01

    From his unusual beginning in ""Defining a vector"" to his final comments on ""What then is a vector?"" author Banesh Hoffmann has written a book that is provocative and unconventional. In his emphasis on the unresolved issue of defining a vector, Hoffmann mixes pure and applied mathematics without using calculus. The result is a treatment that can serve as a supplement and corrective to textbooks, as well as collateral reading in all courses that deal with vectors. Major topics include vectors and the parallelogram law; algebraic notation and basic ideas; vector algebra; scalars and scalar p

  8. Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery

    Directory of Open Access Journals (Sweden)

    Fan Liang

    2013-02-01

    Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.

  9. Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

    Science.gov (United States)

    Nabelek, Daniel P.; Ho, K. C.

    2013-06-01

    The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

  10. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    Science.gov (United States)

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  11. Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

    Directory of Open Access Journals (Sweden)

    Ernest Kissi

    2018-03-01

    Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

  12. Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

    Science.gov (United States)

    Fu, Xiao; Huang, Kejun; Hong, Mingyi; Sidiropoulos, Nicholas D.; So, Anthony Man-Cho

    2017-08-01

    Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities. Unlike principal component analysis (PCA) that handles a single view, (G)CCA is able to integrate information from different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation which has recently gained renewed interest in multilingual processing and speech modeling. The classic MAX-VAR GCCA problem can be solved optimally via eigen-decomposition of a matrix that compounds the (whitened) correlation matrices of the views; but this solution has serious scalability issues, and is not directly amenable to incorporating pertinent structural constraints such as non-negativity and sparsity on the canonical components. We posit regularized MAX-VAR GCCA as a non-convex optimization problem and propose an alternating optimization (AO)-based algorithm to handle it. Our algorithm alternates between {\\em inexact} solutions of a regularized least squares subproblem and a manifold-constrained non-convex subproblem, thereby achieving substantial memory and computational savings. An important benefit of our design is that it can easily handle structure-promoting regularization. We show that the algorithm globally converges to a critical point at a sublinear rate, and approaches a global optimal solution at a linear rate when no regularization is considered. Judiciously designed simulations and large-scale word embedding tasks are employed to showcase the effectiveness of the proposed algorithm.

  13. MONETARY POLICY TRANSMISSION MECHANISM AND TVP-VAR MODEL

    Directory of Open Access Journals (Sweden)

    Andreea ROŞOIU

    2013-12-01

    Full Text Available The transmission of monetary policy to the economy is a subject of major importance for central banks because, by using these measures, central banks can achieve their purpose of ensuring price stability without neglecting the objective of sustainable economic growth. In order to analyze the evolution of the monetary policy transmission mechanism in Romania, a time varying structural vector autoregression model is estimated, by using a Markov Chain Monte Carlo algorithm for the posterior evolution. The conclusions of the empirical study are: both systematic and non-systematic monetary policy have changed during the investigated period of time, the systematic response of the interest rate to shocks in inflation and unemployment being faster over the recent period. Also, non-policy shocks seem more important than interest rate shocks in explaining inflation and unemployment evolution.

  14. Least squares estimation in a simple random coefficient autoregressive model

    DEFF Research Database (Denmark)

    Johansen, S; Lange, T

    2013-01-01

    The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...... we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source...

  15. 4K Video Traffic Prediction using Seasonal Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    D. R. Marković

    2017-06-01

    Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

  16. Asymptotically stable phase synchronization revealed by autoregressive circle maps

    Science.gov (United States)

    Drepper, F. R.

    2000-11-01

    A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.

  17. REGIONAL FIRST ORDER PERIODIC AUTOREGRESSIVE MODELS FOR MONTHLY FLOWS

    Directory of Open Access Journals (Sweden)

    Ceyhun ÖZÇELİK

    2008-01-01

    Full Text Available First order periodic autoregressive models is of mostly used models in modeling of time dependency of hydrological flow processes. In these models, periodicity of the correlogram is preserved as well as time dependency of processes. However, the parameters of these models, namely, inter-monthly lag-1 autocorrelation coefficients may be often estimated erroneously from short samples, since they are statistics of high order moments. Therefore, to constitute a regional model may be a solution that can produce more reliable and decisive estimates, and derive models and model parameters in any required point of the basin considered. In this study, definitions of homogeneous region for lag-1 autocorrelation coefficients are made; five parametric and non parametric models are proposed to set regional models of lag-1 autocorrelation coefficients. Regional models are applied on 30 stream flow gauging stations in Seyhan and Ceyhan basins, and tested by criteria of relative absolute bias, simple and relative root of mean square errors.

  18. Drought Patterns Forecasting using an Auto-Regressive Logistic Model

    Science.gov (United States)

    del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

    2014-12-01

    Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

  19. Elementary vectors

    CERN Document Server

    Wolstenholme, E Œ

    1978-01-01

    Elementary Vectors, Third Edition serves as an introductory course in vector analysis and is intended to present the theoretical and application aspects of vectors. The book covers topics that rigorously explain and provide definitions, principles, equations, and methods in vector analysis. Applications of vector methods to simple kinematical and dynamical problems; central forces and orbits; and solutions to geometrical problems are discussed as well. This edition of the text also provides an appendix, intended for students, which the author hopes to bridge the gap between theory and appl

  20. Structural VAR analysis of monetary transmission mechanism and central bank’s response to equity volatility shock in Taiwan

    OpenAIRE

    Lo, Chi-Sheng

    2016-01-01

    This research applies recursive Structural Vector Auto Regression (SVAR) model with short-run restriction into two kinds of shocks: monetary and volatility. The first SVAR estimates the shock of contractionary monetary policy on Taiwan’s key monthly macroeconomic variables including exports, CPI, exchange rate, money supply, and Taiwan Weighted Stock Exchange (TWSE) Index. The second SVAR estimates the shock of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) volatility of TW...

  1. Characteristics of the transmission of autoregressive sub-patterns in financial time series

    Science.gov (United States)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

    2014-09-01

    There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

  2. Vector analysis

    CERN Document Server

    Brand, Louis

    2006-01-01

    The use of vectors not only simplifies treatments of differential geometry, mechanics, hydrodynamics, and electrodynamics, but also makes mathematical and physical concepts more tangible and easy to grasp. This text for undergraduates was designed as a short introductory course to give students the tools of vector algebra and calculus, as well as a brief glimpse into these subjects' manifold applications. The applications are developed to the extent that the uses of the potential function, both scalar and vector, are fully illustrated. Moreover, the basic postulates of vector analysis are brou

  3. The asymptotic and exact Fisher information matrices of a vector ARMA process

    NARCIS (Netherlands)

    Klein, A.; Melard, G.; Saidi, A.

    2008-01-01

    The exact Fisher information matrix of a Gaussian vector autoregressive-moving average (VARMA) process has been considered for a time series of length N in relation to the exact maximum likelihood estimation method. In this paper it is shown that the Gaussian exact Fisher information matrix

  4. [Susceptibility of Aedes aegypti (L.) strains from Havana to a Bacillus thuringiensis var. israelensis].

    Science.gov (United States)

    Menéndez Díaz, Zulema; Rodríguez Rodríguez, Jinnay; Gato Armas, René; Companioni Ibañez, Ariamys; Díaz Pérez, Manuel; Bruzón Aguila, Rosa Yirian

    2012-01-01

    the integration of chemical and biological methods is one of the strategies for the vector control, due to the existing environmental problems and the concerns of the community as a result of the synthetic organic insecticide actions. The bacterium called Bacillus thuringiensis var. israelensis in liquid formulation has been widely used in the vector control programs in several countries and has shown high efficacy at lab in Cuba. to determine the susceptibility of Aedes aegypti collected in the municipalities of La Habana province to Bacillus thuringiensis var. israelensis. fifteen Aedes aegypti strains, one from each municipality, were used including larvae and pupas collected in 2010 and one reference strain known as Rockefeller. The aqueous formulation of Bacillus thuringiensis var. israelensis (Bactivec, Labiofam, Cuba) was used. The bioassays complied with the World Health Organization guidelines for use of bacterial larvicides in the public health sector. The larval mortality was read after 24 hours and the results were processed by the statistical system SPSS (11.0) through Probit analysis. the evaluated mosquito strains showed high susceptibility to biolarvicide, there were no significant differences in LC50 values of Ae. aegypti strains, neither in the comparison of these values with those of the reference strain. the presented results indicate that the use of Bacillus thuringiensis var. israelensis continues to be a choice for the control of Aedes aegypti larval populations in La Habana province.

  5. The comparison study among several data transformations in autoregressive modeling

    Science.gov (United States)

    Setiyowati, Susi; Waluyo, Ramdhani Try

    2015-12-01

    In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.

  6. An algebraic method for constructing stable and consistent autoregressive filters

    International Nuclear Information System (INIS)

    Harlim, John; Hong, Hoon; Robbins, Jacob L.

    2015-01-01

    In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern

  7. Prediction of municipal solid waste generation using nonlinear autoregressive network.

    Science.gov (United States)

    Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

    2015-12-01

    Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

  8. On the maximum-entropy/autoregressive modeling of time series

    Science.gov (United States)

    Chao, B. F.

    1984-01-01

    The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

  9. Sparse representation based image interpolation with nonlocal autoregressive modeling.

    Science.gov (United States)

    Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

    2013-04-01

    Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

  10. Adaptive Autoregressive Model for Reduction of Noise in SPECT

    Directory of Open Access Journals (Sweden)

    Reijo Takalo

    2015-01-01

    Full Text Available This paper presents improved autoregressive modelling (AR to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM reconstruction images (AR-OSEM-AR method. The performance of this method was compared with filtered back projection (FBP preceded by Butterworth filtering (BW-FBP method and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method. A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR% and the full width at half maximum (FWHM of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.

  11. Biometeorological and autoregressive indices for predicting olive pollen intensity.

    Science.gov (United States)

    Oteros, J; García-Mozo, H; Hervás, C; Galán, C

    2013-03-01

    This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982-2010), a multivariate regression model based on five indices-the index-based model-was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.

  12. Vector velocimeter

    DEFF Research Database (Denmark)

    2012-01-01

    The present invention relates to a compact, reliable and low-cost vector velocimeter for example for determining velocities of particles suspended in a gas or fluid flow, or for determining velocity, displacement, rotation, or vibration of a solid surface, the vector velocimeter comprising a laser...

  13. Consistent and Conservative Model Selection with the Adaptive LASSO in Stationary and Nonstationary Autoregressions

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl

    2016-01-01

    We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...

  14. A THRESHOLD-VAR APPROACH TO ASSESS THE EFFICACY OF THE EU IMPORT REGIME

    Directory of Open Access Journals (Sweden)

    Fabio Gaetano Santeramo

    2014-01-01

    Full Text Available The efficacy of the entry price policy in the European fresh fruits and vegetables sector is not well understood. We investigate econometrically the efficacy of the import policy. The analysis estimates a threshold vector autoregressive model using data on imports the main competing country on the European Union domestic markets. Our results allow concluding on the insulating effects induced by the entry price system. We argue that the quota restrictions are likely to provide larger contribution to the domestic market stabilization. Policy implications are provided.

  15. A structural VAR analysis of electricity consumption and real GDP: Evidence from the G7 countries

    International Nuclear Information System (INIS)

    Narayan, Paresh Kumar; Narayan, Seema; Prasad, Arti

    2008-01-01

    In this paper, we depart from the literature on electricity consumption-real GDP in that for the first time we examine the reaction of real GDP to shocks in electricity consumption. To achieve this goal, we use the structural vector autoregressive (SVAR) model and examine the impact of electricity consumption shocks on real GDP for the G7 countries. We find that except for the USA, electricity consumption has a statistically significant positive impact on real GDP over short horizons. This finding implies that except for the USA, electricity conservation policies will hurt real GDP in the G7 countries

  16. New var reconstruction algorithm exposes high var sequence diversity in a single geographic location in Mali.

    Science.gov (United States)

    Dara, Antoine; Drábek, Elliott F; Travassos, Mark A; Moser, Kara A; Delcher, Arthur L; Su, Qi; Hostelley, Timothy; Coulibaly, Drissa; Daou, Modibo; Dembele, Ahmadou; Diarra, Issa; Kone, Abdoulaye K; Kouriba, Bourema; Laurens, Matthew B; Niangaly, Amadou; Traore, Karim; Tolo, Youssouf; Fraser, Claire M; Thera, Mahamadou A; Djimde, Abdoulaye A; Doumbo, Ogobara K; Plowe, Christopher V; Silva, Joana C

    2017-03-28

    Encoded by the var gene family, highly variable Plasmodium falciparum erythrocyte membrane protein-1 (PfEMP1) proteins mediate tissue-specific cytoadherence of infected erythrocytes, resulting in immune evasion and severe malaria disease. Sequencing and assembling the 40-60 var gene complement for individual infections has been notoriously difficult, impeding molecular epidemiological studies and the assessment of particular var elements as subunit vaccine candidates. We developed and validated a novel algorithm, Exon-Targeted Hybrid Assembly (ETHA), to perform targeted assembly of var gene sequences, based on a combination of Pacific Biosciences and Illumina data. Using ETHA, we characterized the repertoire of var genes in 12 samples from uncomplicated malaria infections in children from a single Malian village and showed them to be as genetically diverse as vars from isolates from around the globe. The gene var2csa, a member of the var family associated with placental malaria pathogenesis, was present in each genome, as were vars previously associated with severe malaria. ETHA, a tool to discover novel var sequences from clinical samples, will aid the understanding of malaria pathogenesis and inform the design of malaria vaccines based on PfEMP1. ETHA is available at: https://sourceforge.net/projects/etha/ .

  17. arXiv Observation of the $\\varXi^{-}_{b}\\to J/\\psi\\varLambda K^{-}$ decay

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Ajaltouni, Ziad; Akar, Simon; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio Augusto; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Andreassi, Guido; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Archilli, Flavio; d'Argent, Philippe; Arnau Romeu, Joan; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Babuschkin, Igor; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baesso, Clarissa; Baker, Sophie; Balagura, Vladislav; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Baryshnikov, Fedor; Baszczyk, Mateusz; Batozskaya, Varvara; Batsukh, Baasansuren; Battista, Vincenzo; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Bel, Lennaert; Bellee, Violaine; Belloli, Nicoletta; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Berezhnoy, Alexander; Bernet, Roland; Bertolin, Alessandro; Betancourt, Christopher; Betti, Federico; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bezshyiko, Iaroslava; Bifani, Simone; Billoir, Pierre; Bird, Thomas; Birnkraut, Alex; Bitadze, Alexander; Bizzeti, Andrea; Blake, Thomas; Blanc, Frederic; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Boettcher, Thomas; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Bordyuzhin, Igor; Borgheresi, Alessio; Borghi, Silvia; Borisyak, Maxim; Borsato, Martino; Bossu, Francesco; Boubdir, Meriem; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Braun, Svende; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Buchanan, Emma; Burr, Christopher; Bursche, Albert; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel Hugo; Capriotti, Lorenzo; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carniti, Paolo; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cavallero, Giovanni; Cenci, Riccardo; Chamont, David; Charles, Matthew; Charpentier, Philippe; Chatzikonstantinidis, Georgios; Chefdeville, Maximilien; Chen, Shanzhen; Cheung, Shu-Faye; Chobanova, Veronika; Chrzaszcz, Marcin; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Cogoni, Violetta; Cojocariu, Lucian; Collazuol, Gianmaria; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombs, George; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Costa Sobral, Cayo Mar; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Crocombe, Andrew; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Da Cunha Marinho, Franciole; Dall'Occo, Elena; Dalseno, Jeremy; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Serio, Marilisa; De Simone, Patrizia; Dean, Cameron Thomas; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Demmer, Moritz; Dendek, Adam; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Dey, Biplab; Di Canto, Angelo; Dijkstra, Hans; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dovbnya, Anatoliy; Dreimanis, Karlis; Dufour, Laurent; Dujany, Giulio; Dungs, Kevin; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Déléage, Nicolas; Easo, Sajan; Ebert, Marcus; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; Ely, Scott; Esen, Sevda; Evans, Hannah Mary; Evans, Timothy; Falabella, Antonio; Farley, Nathanael; Farry, Stephen; Fay, Robert; Fazzini, Davide; Ferguson, Dianne; Fernandez Prieto, Antonio; Ferrari, Fabio; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fini, Rosa Anna; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fleuret, Frederic; Fohl, Klaus; Fontana, Marianna; Fontanelli, Flavio; Forshaw, Dean Charles; Forty, Roger; Franco Lima, Vinicius; Frank, Markus; Frei, Christoph; Fu, Jinlin; Funk, Wolfgang; Furfaro, Emiliano; Färber, Christian; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garcia Martin, Luis Miguel; García Pardiñas, Julián; Garra Tico, Jordi; Garrido, Lluis; Garsed, Philip John; Gascon, David; Gaspar, Clara; Gavardi, Laura; Gazzoni, Giulio; Gerick, David; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianì, Sebastiana; Gibson, Valerie; Girard, Olivier Göran; Giubega, Lavinia-Helena; Gizdov, Konstantin; Gligorov, Vladimir; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gorelov, Igor Vladimirovich; Gotti, Claudio; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graverini, Elena; Graziani, Giacomo; Grecu, Alexandru; Griffith, Peter; Grillo, Lucia; Gruberg Cazon, Barak Raimond; Grünberg, Oliver; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Göbel, Carla; Hadavizadeh, Thomas; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hamilton, Brian; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; Hatch, Mark; He, Jibo; Head, Timothy; Heister, Arno; Hennessy, Karol; Henrard, Pierre; Henry, Louis; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hombach, Christoph; Hopchev, P H; Hulsbergen, Wouter; Humair, Thibaud; Hushchyn, Mikhail; Hutchcroft, David; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jawahery, Abolhassan; Jiang, Feng; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kandybei, Sergii; Karacson, Matthias; Kariuki, James Mwangi; Karodia, Sarah; Kecke, Matthieu; Kelsey, Matthew; Kenzie, Matthew; Ketel, Tjeerd; Khairullin, Egor; Khanji, Basem; Khurewathanakul, Chitsanu; Kirn, Thomas; Klaver, Suzanne; Klimaszewski, Konrad; Koliiev, Serhii; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Kosmyntseva, Alena; Kozachuk, Anastasiia; Kozeiha, Mohamad; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krokovny, Pavel; Kruse, Florian; Krzemien, Wojciech; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kuonen, Axel Kevin; Kurek, Krzysztof; Kvaratskheliya, Tengiz; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lanfranchi, Gaia; Langenbruch, Christoph; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Leflat, Alexander; Lefrançois, Jacques; Lefèvre, Regis; Lemaitre, Florian; Lemos Cid, Edgar; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Tenglin; Li, Yiming; Likhomanenko, Tatiana; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Xuesong; Loh, David; Longstaff, Iain; Lopes, Jose; Lucchesi, Donatella; Lucio Martinez, Miriam; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Lusiani, Alberto; Lyu, Xiao-Rui; Machefert, Frederic; Maciuc, Florin; Maev, Oleg; Maguire, Kevin; Malde, Sneha; Malinin, Alexander; Maltsev, Timofei; Manca, Giulia; Mancinelli, Giampiero; Manning, Peter Michael; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marinangeli, Matthieu; Marino, Pietro; Marks, Jörg; Martellotti, Giuseppe; Martin, Morgan; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massacrier, Laure Marie; Massafferri, André; Matev, Rosen; Mathad, Abhijit; Mathe, Zoltan; Matteuzzi, Clara; Mauri, Andrea; Maurice, Emilie; Maurin, Brice; Mazurov, Alexander; McCann, Michael; McNab, Andrew; McNulty, Ronan; Meadows, Brian; Meier, Frank; Meissner, Marco; Melnychuk, Dmytro; Merk, Marcel; Merli, Andrea; Michielin, Emanuele; Milanes, Diego Alejandro; Minard, Marie-Noelle; Mitzel, Dominik Stefan; Mogini, Andrea; Molina Rodriguez, Josue; Monroy, Ignacio Alberto; Monteil, Stephane; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Morgunova, Olga; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Mulder, Mick; Mussini, Manuel; Müller, Dominik; Müller, Janine; Müller, Katharina; Müller, Vanessa; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nandi, Anita; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Thi Dung; Nguyen-Mau, Chung; Nieswand, Simon; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Nogay, Alla; Novoselov, Alexey; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Ogilvy, Stephen; Oldeman, Rudolf; Onderwater, Gerco; Otalora Goicochea, Juan Martin; Otto, Adam; Owen, Patrick; Oyanguren, Maria Aranzazu; Pais, Preema Rennee; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parker, William; Parkes, Christopher; Passaleva, Giovanni; Pastore, Alessandra; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perret, Pascal; Pescatore, Luca; Petridis, Konstantinos; Petrolini, Alessandro; Petrov, Aleksandr; Petruzzo, Marco; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pikies, Malgorzata; Pinci, Davide; Pistone, Alessandro; Piucci, Alessio; Placinta, Vlad-Mihai; Playfer, Stephen; Plo Casasus, Maximo; Poikela, Tuomas; Polci, Francesco; Poluektov, Anton; Polyakov, Ivan; Polycarpo, Erica; Pomery, Gabriela Johanna; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Poslavskii, Stanislav; Potterat, Cédric; Price, Eugenia; Price, Joseph David; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Quagliani, Renato; Rachwal, Bartolomiej; Rademacker, Jonas; Rama, Matteo; Ramos Pernas, Miguel; Rangel, Murilo; Raniuk, Iurii; Ratnikov, Fedor; Raven, Gerhard; Redi, Federico; Reichert, Stefanie; dos Reis, Alberto; Remon Alepuz, Clara; Renaudin, Victor; Ricciardi, Stefania; Richards, Sophie; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vicente; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Lopez, Jairo Alexis; Rodriguez Perez, Pablo; Rogozhnikov, Alexey; Roiser, Stefan; Rollings, Alexandra Paige; Romanovskiy, Vladimir; Romero Vidal, Antonio; Ronayne, John William; Rotondo, Marcello; Rudolph, Matthew Scott; Ruf, Thomas; Ruiz Valls, Pablo; Saborido Silva, Juan Jose; Sadykhov, Elnur; Sagidova, Naylya; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santimaria, Marco; Santovetti, Emanuele; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saunders, Daniel Martin; Savrina, Darya; Schael, Stefan; Schellenberg, Margarete; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmelzer, Timon; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schubert, Konstantin; Schubiger, Maxime; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Semennikov, Alexander; Sergi, Antonino; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Siddi, Benedetto Gianluca; Silva Coutinho, Rafael; Silva de Oliveira, Luiz Gustavo; Simi, Gabriele; Simone, Saverio; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Eluned; Smith, Iwan Thomas; Smith, Jackson; Smith, Mark; Snoek, Hella; Soares Lavra, Lais; Sokoloff, Michael; Soler, Paul; Souza De Paula, Bruno; Spaan, Bernhard; Spradlin, Patrick; Sridharan, Srikanth; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Stefko, Pavol; Stefkova, Slavorima; Steinkamp, Olaf; Stemmle, Simon; Stenyakin, Oleg; Stevens, Holger; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Syropoulos, Vasileios; Szczekowski, Marek; Szumlak, Tomasz; T'Jampens, Stephane; Tayduganov, Andrey; Tekampe, Tobias; Tellarini, Giulia; Teubert, Frederic; Thomas, Eric; van Tilburg, Jeroen; Tilley, Matthew James; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Toriello, Francis; Tournefier, Edwige; Tourneur, Stephane; Trabelsi, Karim; Traill, Murdo; Tran, Minh Tâm; Tresch, Marco; Trisovic, Ana; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tully, Alison; Tuning, Niels; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vacca, Claudia; Vagnoni, Vincenzo; Valassi, Andrea; Valat, Sebastien; Valenti, Giovanni; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vecchi, Stefania; van Veghel, Maarten; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Venkateswaran, Aravindhan; Vernet, Maxime; Vesterinen, Mika; Viana Barbosa, Joao Vitor; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Viemann, Harald; Vilasis-Cardona, Xavier; Vitti, Marcela; Volkov, Vladimir; Vollhardt, Achim; Voneki, Balazs; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; de Vries, Jacco; Vázquez Sierra, Carlos; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wang, Jianchun; Ward, David; Wark, Heather Mckenzie; Watson, Nigel; Websdale, David; Weiden, Andreas; Whitehead, Mark; Wicht, Jean; Wilkinson, Guy; Wilkinson, Michael; Williams, Mark Richard James; Williams, Matthew; Williams, Mike; Williams, Timothy; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wraight, Kenneth; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Xu, Zhirui; Yang, Zhenwei; Yao, Yuezhe; Yin, Hang; Yu, Jiesheng; Yuan, Xuhao; Yushchenko, Oleg; Zarebski, Kristian Alexander; Zavertyaev, Mikhail; Zhang, Liming; Zhang, Yanxi; Zhang, Yu; Zhelezov, Alexey; Zheng, Yangheng; Zhu, Xianglei; Zhukov, Valery; Zucchelli, Stefano

    2017-09-10

    The observation of the decay $\\varXi_{b}^{-}\\to J/\\psi\\varLambda K^{-}$ is reported, using a data sample corresponding to an integrated luminosity of $3~\\mathrm{fb}^{-1}$, collected by the LHCb detector in $pp$ collisions at centre-of-mass energies of $7$ and $8~\\mathrm{TeV}$. The production rate of $\\varXi_{b}^{-}$ baryons detected in the decay $\\varXi_{b}^{-}\\to J/\\psi\\varLambda K^{-}$ is measured relative to that of $\\varLambda_{b}^{0}$ baryons using the decay $\\varLambda_{b}^{0}\\to J/\\psi \\varLambda$. Integrated over the $b$-baryon transverse momentum $p_{\\rm T}<25~\\mathrm{GeV/}c $ and rapidity $2.0 < y < 4.5$, the measured ratio is \\begin{equation*} \\frac{f_{\\varXi_{b}^{-}}}{f_{\\varLambda_{b}^{0}}}\\frac{\\mathcal{B}(\\varXi_{b}^{-}\\to J/\\psi\\varLambda K^{-})}{\\mathcal{B}(\\varLambda_{b}^{0}\\to J/\\psi \\varLambda)}=(4.19\\pm 0.29~(\\mathrm{stat})\\pm0.14~(\\mathrm{syst}))\\times 10^{-2}, \\end{equation*}where $f_{\\varXi_{b}^{-}}$ and $f_{\\varLambda_{b}^{0}}$ are the fragmentation fractions of $b\\to\\varXi_{...

  18. Cloning vector

    Science.gov (United States)

    Guilfoyle, Richard A.; Smith, Lloyd M.

    1994-01-01

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site.

  19. Cloning vector

    Science.gov (United States)

    Guilfoyle, R.A.; Smith, L.M.

    1994-12-27

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site. 2 figures.

  20. Equivalent Vectors

    Science.gov (United States)

    Levine, Robert

    2004-01-01

    The cross-product is a mathematical operation that is performed between two 3-dimensional vectors. The result is a vector that is orthogonal or perpendicular to both of them. Learning about this for the first time while taking Calculus-III, the class was taught that if AxB = AxC, it does not necessarily follow that B = C. This seemed baffling. The…

  1. Taxonomical studies on endemic scorzonera pygmaea var. pygmaea and var. nutans stat. nov. (asteraceae) from turkey

    International Nuclear Information System (INIS)

    Koyuncu, O.; Kus, G.

    2014-01-01

    The taxonomic status of Scorzonera pygmaea var. pygmaea and var. nutans belonging to the tribe. cichoreae (Asteraceae). S. pygmaea samples were collected from Arayit mountain. We suggest that these two subspecies should be classified as varietes because of their morphological and anatomical characteristics, ecological and geographical similarities. Moreover being together in the same localities of these under species taxa supports our opinion, i.e. S. pygmaea Sibth. and Sm. var. pygmaea stat. nov. and S. pygmaea Sibth. and Sm. var. nutans (Czeczott) O. Koyuncu and Yaylac, stat. nov. (author)

  2. Potential for wind extraction from 4D-Var assimilation of aerosols and moisture

    Science.gov (United States)

    Zaplotnik, Žiga; Žagar, Nedjeljka

    2017-04-01

    We discuss the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from observations of atmospheric tracers and the mass field through internal model dynamics and the multivariate relationships in the background-error term for 4D-Var. The presence of non-linear moist dynamics makes the wind retrieval from tracers very difficult. On the other hand, it has been shown that moisture observations strongly influence both tropical and mid-latitude wind field in 4D-Var. We present an intermediate complexity model that describes nonlinear interactions between the wind, temperature, aerosols and moisture including their sinks and sources in the framework of the so-called first baroclinic mode atmosphere envisaged by A. Gill. Aerosol physical processes, which are included in the model, are the non-linear advection, diffusion and sources and sinks that exist as dry and wet deposition and diffusion. Precipitation is parametrized according to the Betts-Miller scheme. The control vector for 4D-Var includes aerosols, moisture and the three dynamical variables. The former is analysed univariately whereas wind field and mass field are analysed in a multivariate fashion taking into account quasi-geostrophic and unbalanced dynamics. The OSSE type of studies are performed for the tropical region to assess the ability of 4D-Var to extract wind-field information from the time series of observations of tracers as a function of the flow nonlinearity, the observations density and the length of the assimilation window (12 hours and 24 hours), in dry and moist environment. Results show that the 4D-Var assimilation of aerosols and temperature data is beneficial for the wind analysis with analysis errors strongly dependent on the moist processes and reliable background-error covariances.

  3. Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

    Directory of Open Access Journals (Sweden)

    Simionescu Mihaela

    2015-06-01

    Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

  4. "Var Teatre"--A Pioneer Turns 40.

    Science.gov (United States)

    Jones, Pamela L.

    1984-01-01

    Describes the Stockholm Municipal Youth and Children's theatre ("Var Teatre"), an institution of 14 theatres and attendant professional staff devoted exclusively to drama activities for children and teenagers. (PD)

  5. Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks

    Science.gov (United States)

    2014-03-27

    series. Chakraborty et al. (1992) modeled flour prices over an eight year period for the cities of Buffalo, Minneapolis and Kansas City via a neural...on stock and commodity market prices (Kaastra & Boyd, 1996) with a goal of discovering non-linear relationships via ANNs which might provide an...Time Series A vector of past observations from a specific time interval is an example of a time series. For example, monthly stock prices from 2000

  6. 4D-Var Developement at GMAO

    Science.gov (United States)

    Pelc, Joanna S.; Todling, Ricardo; Akkraoui, Amal El

    2014-01-01

    The Global Modeling and Assimilation Offce (GMAO) is currently using an IAU-based 3D-Var data assimilation system. GMAO has been experimenting with a 3D-Var-hybrid version of its data assimilation system (DAS) for over a year now, which will soon become operational and it will rapidly progress toward a 4D-EnVar. Concurrently, the machinery to exercise traditional 4DVar is in place and it is desirable to have a comparison of the traditional 4D approach with the other available options, and evaluate their performance in the Goddard Earth Observing System (GEOS) DAS. This work will also explore the possibility for constructing a reduced order model (ROM) to make traditional 4D-Var computationally attractive for increasing model resolutions. Part of the research on ROM will be to search for a suitably acceptable space to carry on the corresponding reduction. This poster illustrates how the IAU-based 4D-Var assimilation compares with our currently used IAU-based 3D-Var.

  7. To center or not to center? Investigating inertia with a multilevel autoregressive model

    Directory of Open Access Journals (Sweden)

    Ellen L. Hamaker

    2015-01-01

    Full Text Available Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion, cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship. This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction, cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

  8. Vector geometry

    CERN Document Server

    Robinson, Gilbert de B

    2011-01-01

    This brief undergraduate-level text by a prominent Cambridge-educated mathematician explores the relationship between algebra and geometry. An elementary course in plane geometry is the sole requirement for Gilbert de B. Robinson's text, which is the result of several years of teaching and learning the most effective methods from discussions with students. Topics include lines and planes, determinants and linear equations, matrices, groups and linear transformations, and vectors and vector spaces. Additional subjects range from conics and quadrics to homogeneous coordinates and projective geom

  9. Weinig VVT-instellingen met VAR : onderzoek naar VAR's in Nederland

    NARCIS (Netherlands)

    Corina de Feijter; Pieterbas Lalleman

    V&VN is op zoek gegaan naar alle actieve Verpleegkundige en/of Verzorgende Adviesraden (VAR) in Nederland. Het blijkt dat in totaal 144 zorginstellingen (24%) een VAR hebben. Vooral in de VVT-sector is het aantal VAR’s laag: 13%.

  10. Molecular and physiological diversity among Verticillium fungicola var. fungicola and var. aleophilum

    NARCIS (Netherlands)

    Largeteau, M.L.; Baars, J.J.P.; Savoie, J.M.

    2006-01-01

    The genetic and physiological variability of Verticillium fungicola var. aleophilum responsible for Agaricus bisporus dry bubble disease in North America is well documented but little is known about the var. fungicola affecting European crops. Variability was assessed within this variety and

  11. Over de verspreiding binnen Nederland van Bidens connata var. fallax (Warnst.) Sherf en var. anomala Farw

    NARCIS (Netherlands)

    Ballintijn, Koos (J.) F.

    1997-01-01

    On the basis of recent observations and study of herbarium material it might tentatively be concluded that var. anomala (with erecto-patent barb hairs on achenes as well as on the bristles) is becoming more common since 1954 and might be locally replacing the hitherto more common var. fallax (with

  12. Media-politics interaction in times of economic crisis: a comparative study of Spain and the Netherlands

    NARCIS (Netherlands)

    Vliegenthart, R.; Mena, N.

    2013-01-01

    This paper investigates the multi-directional causal relationships between stock market ratings, negative economic coverage in two national newspapers and parliamentary questions addressing negative aspects of the economy in Spain and the Netherlands. Weekly-level Vector Autoregression (VAR)

  13. Afrika Statistika ISSN 2316-090X Multivariate Analysis of Rwanda ...

    African Journals Online (AJOL)

    Consumer Price Index (CPI), Exchange Rate and Nominal Growth. Domestic ... Economic Indicators using Vector Autoregressive (VAR) Model. 1540 ... useful for describing the dynamic behavior of economic and financial time series and for.

  14. Seasonal Forecasting of Agriculture Gross Domestic Production in Iran: Application of Periodic Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Mohammad Ghahremanzadeh

    2014-06-01

    Full Text Available Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR, as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004. The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996 was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use periodic autoregressive model for the future studies.

  15. Forecasting performance of smooth transition autoregressive (STAR model on travel and leisure stock index

    Directory of Open Access Journals (Sweden)

    Usman M. Umer

    2018-06-01

    Full Text Available Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR and linear autoregressive (AR models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index. Keywords: Nonlinear time-series, Out-of-sample forecasting, Smooth transition autoregressive, Travel and leisure

  16. VECTOR INTEGRATION

    NARCIS (Netherlands)

    Thomas, E. G. F.

    2012-01-01

    This paper deals with the theory of integration of scalar functions with respect to a measure with values in a, not necessarily locally convex, topological vector space. It focuses on the extension of such integrals from bounded measurable functions to the class of integrable functions, proving

  17. Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments

    Directory of Open Access Journals (Sweden)

    Fei Jin

    2013-05-01

    Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.

  18. Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression

    Directory of Open Access Journals (Sweden)

    Lili Li

    2016-01-01

    Full Text Available We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7 and China. The quantile autoregression model (QAR enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.

  19. A VAR Analysis of Kenya’s Monetary Policy Transmission Mechanism; How Does the Central Bank’s REPO Rate Affect the Economy?

    OpenAIRE

    Kevin C Cheng

    2006-01-01

    This paper examines the impact of a monetary policy shock on output, prices, and the nominal effective exchange rate for Kenya using data during 1997–2005. Based on techniques commonly used in the vector autoregression literature, the main results suggest that an exogenous increase in the short-term interest rate tends to be followed by a decline in prices and appreciation in the nominal exchange rate, but has insignificant impact on output. Moreover, the paper finds that variations in the sh...

  20. Hybrid VAR compensator with improved efficiency

    Directory of Open Access Journals (Sweden)

    V. V. Burlaka

    2015-03-01

    Full Text Available In modern electrical networks thyristor-switched capacitors (TSC are most used devices for VAR compensation. These devices don’t contain rotating parts and mechanical contacts, provide a stepwise control of reactive power and no generation of harmonics to the network. However, with the help of TSC it’s not possible to ensure smooth control of reactive power and capacitor banks (CB are exposed to the negative impact of higher harmonic components of the network voltage. Hybrid VAR compensator don’t have such drawbacks. It consists of active filter (AF and capacitor bank with discrete regulation. The main drawback of such systems is the necessity of accessing all six terminals of CB, while most of them are manufactured with three terminals, internally delta-connected. In the article, the topology and control system of hybrid VAR compensator free from beforementioned drawback, is proposed. The control system provides operating modes of overcompensation or undercompensation reactive power. VAR distribution regulator performs redistribution of reactive power between active filter and capacitor banks with the condition to minimize active filter’s power. Scheme of the hybrid VAR compensator, which includes a three-phase three-terminal delta-connected capacitor banks, is shown. Proposed approach allows to provide smooth control of reactive power, isolate the capacitor bank from harmonic currents and use a more effective low-voltage power components

  1. An introduction to vectors, vector operators and vector analysis

    CERN Document Server

    Joag, Pramod S

    2016-01-01

    Ideal for undergraduate and graduate students of science and engineering, this book covers fundamental concepts of vectors and their applications in a single volume. The first unit deals with basic formulation, both conceptual and theoretical. It discusses applications of algebraic operations, Levi-Civita notation, and curvilinear coordinate systems like spherical polar and parabolic systems and structures, and analytical geometry of curves and surfaces. The second unit delves into the algebra of operators and their types and also explains the equivalence between the algebra of vector operators and the algebra of matrices. Formulation of eigen vectors and eigen values of a linear vector operator are elaborated using vector algebra. The third unit deals with vector analysis, discussing vector valued functions of a scalar variable and functions of vector argument (both scalar valued and vector valued), thus covering both the scalar vector fields and vector integration.

  2. Haavelmo's Probability Approach and the Cointegrated VAR

    DEFF Research Database (Denmark)

    Juselius, Katarina

    Some key econometric concepts and problems addressed by Trygve Haavelmo and Ragnar Frisch are discussed within the general frame- work of a cointegrated VAR. The focus is on problems typical of time- series data such as multicollinearity, spurious correlation and regres- sion results, time......) the plausibility of the multivari- ate normality assumption underlying the VAR, (3) cointegration as a solution to the problem of spurious correlation and multicollinearity when data contain deterministic and stochastic trends, (4) the exis- tence of a universe, (5) the association between Frisch’s con...

  3. Fitting multistate transition models with autoregressive logistic regression : Supervised exercise in intermittent claudication

    NARCIS (Netherlands)

    de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M

    1998-01-01

    The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a

  4. Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input

    OpenAIRE

    Addo, Peter Martey

    2014-01-01

    This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.

  5. Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes

    NARCIS (Netherlands)

    Janssen, A.J.E.M.; Veldhuis, R.N.J.; Vries, L.B.

    1986-01-01

    The authors present an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a

  6. Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes

    NARCIS (Netherlands)

    Janssen, A.J.E.M.; Veldhuis, Raymond N.J.; Vries, Lodewijk B.

    1986-01-01

    This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a

  7. On the Oracle Property of the Adaptive LASSO in Stationary and Nonstationary Autoregressions

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl

    We show that the Adaptive LASSO is oracle efficient in stationary and non-stationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...

  8. Rediscovery of Impatiens laevigata var. grandifolia (Balsaminaceae from NE India

    Directory of Open Access Journals (Sweden)

    R. Gogoi

    2013-12-01

    Full Text Available Impatiens laevigata var. grandifolia Hook.f. rediscovered after a lapse of 139 years from Lohit district of Arunachal Pradesh. Earlier it was known only – from its type locality in Manipur. Detailed morphological description of I. laevigata var. laevigata and var. grandifolia have been provided based on fresh plant collections.

  9. B. oleracea var. capitata monosomic and disomic alien

    Indian Academy of Sciences (India)

    Five monosomic alien addition lines (MAALs) of Brassica rapa ssp. pekinensis – B. oleracea var. capitata were obtained by hybridization and backcrossing between B. rapa ssp. pekinensis (female parent) and B. oleracea var. capitata. The alien linkage groups were identified using 42 B. oleracea var. capitata linkage ...

  10. Combined effect of seaweed (Sargassum wightii) and Bacillus thuringiensis var. israelensis on the coastal mosquito,Anopheles sundaicus, in Tamil Nadu, India

    Science.gov (United States)

    Studies were made of the extract of Sargassum wightii combined with Bacillus thuringiensis var. israelensis (Bti) for control of the malaria vector Anopheles sundaicus. Treatment of mosquito larvae with 0.001% S. wightii extract indicated median lethal concentrations (LC50) of 88, 73, 134, 156, and...

  11. Inflation adjustment in the open economy

    DEFF Research Database (Denmark)

    Nielsen, Heino Bohn; Bowdler, Christopher

    2006-01-01

    This paper estimates a cointegrated vector autoregressive (VAR) model for UK data on consumer prices, unit labour costs, import prices and real consumption growth. The estimated VAR indicates that the nominal variables are characterised by I(2) trends, and that a linear combination of these proce......This paper estimates a cointegrated vector autoregressive (VAR) model for UK data on consumer prices, unit labour costs, import prices and real consumption growth. The estimated VAR indicates that the nominal variables are characterised by I(2) trends, and that a linear combination...

  12. Linear and non-linear autoregressive models for short-term wind speed forecasting

    International Nuclear Information System (INIS)

    Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

    2016-01-01

    Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

  13. Teucrium pruinosum var. aksarayense var. nov. (Lamiaceae from Central Anatolia, Turkey

    Directory of Open Access Journals (Sweden)

    Muhittin Dinç

    2016-06-01

    Full Text Available Teucrium pruinosum var. aksarayense M. Dinç & S. Doğu (Lamiaceae, a new variety from Aksaray in Central Anatolia, is described and illustrated. The new variety is similar to the typical one in its calyx teeth uncinate at tip and subequal to the tube with conspicious midvein. It is readily distinguished from var. pruinosum by its general appearance, indumentum, and floral organ pigmentation. The map showing the distributions of the varieties was given.

  14. What is Orobanche haenseleri var. deludens Beck?

    Directory of Open Access Journals (Sweden)

    Pujadas Salvà, Antonio J.

    2004-12-01

    Full Text Available Orobanche haenseleri var. deludens Beck (Orobanchaceae, a problematic taxon described from Algeciras (Cádiz, S Spain is here identified after studying the original material of Wolley-Dod (BM 4476. It is considered to be the same as O. austrohispanica M.J.Y. Foley and better included, as a variety, under O. gracilis Sm. The new combination O. gracilis var. deludens (Beck A. Pujadas is consequently proposed. It mainly parasites Ulex (Fabaceae in the western Mediterranean Region (Iberian Peninsula and NW Africa.Se identifica Orobanche haenseleri var. deludens Beck (Orobanchaceae, un taxon conflictivo descrito de Algeciras (Cádiz, sur de España, a partir del análisis del material original de Wolley-Dod (BM 4476. Se considera que es lo mismo que O. austrohispanica M.J.Y. Foley, y se incluye en O. gracilis Sm. con rango varietal. Se propone la nueva combinación O. gracilis var. deludens (Beck A. Pujadas. Parasita principalmente a especies del género Ulex (Fabaceae en la Región Mediterránea Occidental (Península Ibérica y noroeste de África.

  15. Is Solanum ferox var. ferox (Solanaceae) extinct?

    NARCIS (Netherlands)

    Heiser, C.B.

    2001-01-01

    In 1995 I wrote letters to over 50 people (botanists, agricultural scientists, and former students of Indiana University) in south-eastern Asia trying to obtain a few seeds of Solanumferox L. var. ferox (S. involucratum Blume). I had over 25 replies, five of which included seeds, but none of the

  16. Anogeissus sericea var. nummalaria King ex Duthie

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 8; Issue 4. Anogeissus sericea var. nummalaria King ex Duthie. Flowering Trees Volume 8 Issue 4 April 2003 pp 89-89. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/008/04/0089-0089. Resonance ...

  17. Var der noget kapløb?

    DEFF Research Database (Denmark)

    Gimpel, Denise; Nielsen, Bent

    2006-01-01

    Hvorfor faldt Kina bagud i kapløbet med Vesten?« spørger David Favrholdt i sidste nummer af Weekendavisen. Var der overhovedet noget kapløb? Forudsætter et kapløb ikke, at man har et fælles mål? Spørgsmålet implicerer, at Vesten har vundet og indplacerer samtidig klodens øvrige kulturer i forhold....... Som en af 1900-tallets største sinologer A. C. Graham skriver i sin bog Disputers of the Tao (La Salle, Ill., 1989) var den videnskabelige revolution »en unik og kompleks begivenhed, som var afhængig af en mangfoldighed af sociale og andre betingelser, herunder et sammenfald af opdagelser (græske......, indiske, kinesiske, arabiske, næsten ingen romerske) koncentreret om kombinationen af indiske tal og aritmetik og græsk logik og geometri« (s. 317). En unik begivenhed, som er forekommet én gang i verdenshistorien. Det giver derfor ingen mening at spørge, hvorfor det ikke skete andre steder. Hvis det var...

  18. Analysis list: Su(var)205 [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Su(var)205 Adult,Embryo,Larvae + dm3 http://dbarchive.biosciencedbc.jp/kyushu-u/dm3.../target/Su(var)205.1.tsv http://dbarchive.biosciencedbc.jp/kyushu-u/dm3/target/Su(var)205.5.tsv http://dbarc...hive.biosciencedbc.jp/kyushu-u/dm3/target/Su(var)205.10.tsv http://dbarchive.biosciencedbc.jp/kyushu-u/dm3/c...olo/Su(var)205.Adult.tsv,http://dbarchive.biosciencedbc.jp/kyushu-u/dm3/colo/Su(var)205.Embryo.tsv,http:...//dbarchive.biosciencedbc.jp/kyushu-u/dm3/colo/Su(var)205.Larvae.tsv http://dbarchive

  19. An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

    Directory of Open Access Journals (Sweden)

    Luis Gonzaga Baca Ruiz

    2016-08-01

    Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

  20. Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration

    International Nuclear Information System (INIS)

    Yao, Ruigen; Pakzad, Shamim N

    2014-01-01

    In the past few decades many types of structural damage indices based on structural health monitoring signals have been proposed, requiring performance evaluation and comparison studies on these indices in a quantitative manner. One tool to help accomplish this objective is analytical sensitivity analysis, which has been successfully used to evaluate the influences of system operational parameters on observable characteristics in many fields of study. In this paper, the sensitivity expressions of two damage features, namely the Mahalanobis distance of autoregressive coefficients and the Cosh distance of autoregressive spectra, will be derived with respect to both structural damage and measurement noise level. The effectiveness of the proposed methods is illustrated in a numerical case study on a 10-DOF system, where their results are compared with those from direct simulation and theoretical calculation. (paper)

  1. Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

    Science.gov (United States)

    Wang, Qijie

    2015-08-01

    The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

  2. Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

    Science.gov (United States)

    Liu, Q. B.; Wang, Q. J.; Lei, M. F.

    2015-09-01

    It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

  3. A fractionally cointegrated VAR analysis of price discovery in commodity futures markets

    DEFF Research Database (Denmark)

    Dolatabadi, Sepideh; Nielsen, Morten Ørregaard; Xu, Ke

    straightforward examination of the adjustment coefficients. In our empirical analysis we use the data from Figuerola-Ferretti and Gonzalo (2010), who conduct a similar analysis using the usual (non-fractional) CVAR model. Our first finding is that, for all markets except copper, the fractional integration......In this paper we apply the recently developed fractionally cointegrated vector autoregressive (FCVAR) model to analyze price discovery in the spot and futures markets for five non-ferrous metals (aluminium, copper, lead, nickel, and zinc). The FCVAR model allows for long memory (fractional...... to the results from the non-fractional model, we find slightly more evidence of price discovery in the spot market. Specifically, using standard likelihood ratio tests, we do not reject the hypothesis that price discovery takes place exclusively in the spot (futures) market for copper, lead, and zinc (aluminium...

  4. Time series analysis of wind speed using VAR and the generalized impulse response technique

    Energy Technology Data Exchange (ETDEWEB)

    Ewing, Bradley T. [Area of Information Systems and Quantitative Sciences, Rawls College of Business and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX 79409-2101 (United States); Kruse, Jamie Brown [Center for Natural Hazard Research, East Carolina University, Greenville, NC (United States); Schroeder, John L. [Department of Geosciences and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States); Smith, Douglas A. [Department of Civil Engineering and Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States)

    2007-03-15

    This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an 'own' shock and led to greatest persistence in the responses of the other wind series. (author)

  5. Housing market volatility in the OECD area: Evidence from VAR based return decompositions

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    . For the majority of countries news about future returns is the main driver, and both real interest rates and risk premia play an important role in accounting for housing market volatility. Bivariate cross-country correlations and principal components analyses indicate that part of the return movements have......Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash ‡ow (rent) news and discount rate (return) news. Only for two countries - Germany and Ireland - do changing expectations of future rents play a dominating role in explaining housing return volatility...... a common factor among the majority of countries. However, in a minority of countries (Germany, Japan, and the Netherlands) return movements have been basically unrelated to return movements in other countries....

  6. Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

    Science.gov (United States)

    Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

    2018-06-01

    Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

  7. Forecasting economy with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn

    OpenAIRE

    Bušs, Ginters

    2009-01-01

    Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to varia...

  8. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

    Directory of Open Access Journals (Sweden)

    Sun Zhangzhen

    2012-08-01

    Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

  9. Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

    Science.gov (United States)

    Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

    2018-03-01

    Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

  10. Estimation of the order of an autoregressive time series: a Bayesian approach

    International Nuclear Information System (INIS)

    Robb, L.J.

    1980-01-01

    Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases

  11. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution.

    Science.gov (United States)

    Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K

    2014-06-01

    Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

  12. A Structured VAR under Changing Monetary Policy

    DEFF Research Database (Denmark)

    Juselius, Katarina

    The empirical analysis is mainly concerned with the aggregate demand for money relation as part of a small macroeconomic system. Using the theory of cointegrated VAR models for I(2) data the long-run relationships in the data are first investigated, and the ML-estimates of the corresponding coint...... the effects of capital liberalization on the determination of money, income, prices, and interest rates in a small open economy...

  13. Survival and infectivity of Sarcoptes scabiei var. canis and var. hominis.

    Science.gov (United States)

    Arlian, L G; Runyan, R A; Achar, S; Estes, S A

    1984-08-01

    Sarcoptes scabiei var. canis served as a suitable model for the study of S. scabiei var. hominis survival. S. scabiei var. canis and var. hominis mites were found to survive off the host for 24 to 36 hours at room conditions (21 degrees C and 40% to 80% relative humidity [RH]), and the canine variety survived 19 days at 10 degrees C and 97% RH. Female mites survived decidedly longer than male mites at comparable conditions. Generally, higher RH values and lower temperatures favored survival, whereas higher temperature and lower RH led to early death. Most canine scabies mites that were held off the host for 36 hours at 75% RH and 22 degrees to 24 degrees C remained infective and penetrated when returned to the host. Live mites of the human variety that were recovered from bed linen slept on by infested patients would also penetrate a host after being held off a host for 96 hours in alternating 12-hour periods of room conditions and refrigeration. Penetration required less than 30 minutes for all life stages of both varieties, and it was accomplished by a mite secretion that dissolved the host tissue. Dislodged mites, particularly those in close proximity to the source, can be a likely source of infestation.

  14. Nonlinear Autoregressive

    African Journals Online (AJOL)

    2017-09-10

    Sep 10, 2017 ... The NAR identification process is done in two steps namely model structure selection and parameter .... with MATLAB 2014a as the development platform. ..... on Modeling, Simulation and Applied Optimization, 2011, pp. 1-5.

  15. A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships

    Directory of Open Access Journals (Sweden)

    Shuang Guan

    2017-10-01

    Full Text Available Many of the existing autoregressive moving average (ARMA forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs for a two-factor first-order autoregressive (AR(1 model and forecasting the training data with the AR(1 model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m model. Lastly, we forecasted test data with the ARMA(1,m model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI from 2001 to 2015 and the international gold price from 2000 to 2010.

  16. Cardenolide glycosides from Elaeodendron australe var. integrifolium.

    Science.gov (United States)

    Butler, Mark S; Towerzey, Leanne; Pham, Ngoc B; Hyde, Edward; Wadi, Sao Khemar; Guymer, Gordon P; Quinn, Ronald J

    2014-02-01

    Extracts from dried leaf and stems of Elaeodendron australe var. integrifolium (Celastraceae) collected in South East Queensland, Australia, were active in an assay that measured Ca(2+) driven expression of IL-2/luciferase designed to identify inhibitors of the ICRAC channel. Bioassay-guided isolation using C18 and polyamide column chromatography, HPLC (Phenyl and C18) and centrifugal partition chromatography (CPC) led to the isolation of digitoxigenin (1) and three cardenolide glycosides, glucoside 2, quinovoside 3 and the new natural product xyloside 4, as the active components with low nM activity in the reporter assay. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging

    NARCIS (Netherlands)

    R.W. Strachan (Rodney); H.K. van Dijk (Herman)

    2010-01-01

    textabstractThe empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class of vector autoregressive (VAR) processes. The linear VAR model is

  18. Kronecker-ARX models in identifying (2D) spatial-temporal systems

    NARCIS (Netherlands)

    Sinquin, B.; Verhaegen, M.H.G.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of

  19. Identification and Characterization of the Spodoptera Su(var) 3-9 Histone H3K9 trimethyltransferase and Its Effect in AcMNPV Infection

    Science.gov (United States)

    Li, Binbin; Li, Sisi; Yin, Juan; Zhong, Jiang

    2013-01-01

    Histone H3-lysine9 (H3K9) trimethyltransferase gene Su(var) 3-9 was cloned and identified in three Spodoptera insects, Spodoptera frugiperda ( S . frugiperda ), S . exigua and S . litura . Sequence analysis showed that Spodoptera Su(var) 3-9 is highly conserved evolutionarily. Su(var) 3-9 protein was found to be localized in the nucleus in Sf9 cells, and interact with histone H3, and the heterochromatin protein 1a (HP1a) and HP1b. A dose-dependent enzymatic activity was found at both 27 °C and 37 °C in vitro, with higher activity at 27 °C. Addition of specific inhibitor chaetocin resulted in decreased histone methylation level and host chromatin relaxation. In contrast, overexpression of Su(var) 3-9 caused increased histone methylation level and cellular genome compaction. In AcMNV-infected Sf9 cells, the transcription of Su(var) 3-9 increased at late time of infection, although the mRNA levels of most cellular genes decreased. Pre-treatment of Sf9 cells with chaetocin speeded up viral DNA replication, and increased the transcription level of a variety of virus genes, whereas in Sf9 cells pre-transformed with Su(var) 3-9 expression vector, viral DNA replication slow down slightly. These findings suggest that Su(var) 3-9 might participate in the viral genes expression an genome replication repression during AcMNPV infection. It provided a new insight for the understanding virus–host interaction mechanism. PMID:23894480

  20. Identification and characterization of the Spodoptera Su(var) 3-9 histone H3K9 trimethyltransferase and its effect in AcMNPV infection.

    Science.gov (United States)

    Li, Binbin; Li, Sisi; Yin, Juan; Zhong, Jiang

    2013-01-01

    Histone H3-lysine(9) (H3K9) trimethyltransferase gene Su(var) 3-9 was cloned and identified in three Spodoptera insects, Spodopterafrugiperda (S. frugiperda), S. exigua and S. litura. Sequence analysis showed that Spodoptera Su(var) 3-9 is highly conserved evolutionarily. Su(var) 3-9 protein was found to be localized in the nucleus in Sf9 cells, and interact with histone H3, and the heterochromatin protein 1a (HP1a) and HP1b. A dose-dependent enzymatic activity was found at both 27 °C and 37 °C in vitro, with higher activity at 27 °C. Addition of specific inhibitor chaetocin resulted in decreased histone methylation level and host chromatin relaxation. In contrast, overexpression of Su(var) 3-9 caused increased histone methylation level and cellular genome compaction. In AcMNV-infected Sf9 cells, the transcription of Su(var) 3-9 increased at late time of infection, although the mRNA levels of most cellular genes decreased. Pre-treatment of Sf9 cells with chaetocin speeded up viral DNA replication, and increased the transcription level of a variety of virus genes, whereas in Sf9 cells pre-transformed with Su(var) 3-9 expression vector, viral DNA replication slow down slightly. These findings suggest that Su(var) 3-9 might participate in the viral genes expression an genome replication repression during AcMNPV infection. It provided a new insight for the understanding virus-host interaction mechanism.

  1. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

    International Nuclear Information System (INIS)

    Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

    2010-01-01

    Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

  2. Insurance-growth nexus in Ghana: An autoregressive distributed lag bounds cointegration approach

    Directory of Open Access Journals (Sweden)

    Abdul Latif Alhassan

    2014-12-01

    Full Text Available This paper examines the long-run causal relationship between insurance penetration and economic growth in Ghana from 1990 to 2010. Using the autoregressive distributed lag (ARDL bounds approach to cointegration by Pesaran et al. (1996, 2001, the study finds a long-run positive relationship between insurance penetration and economic growth which implies that funds mobilized from insurance business have a long run impact on economic growth. A unidirectional causality was found to run from aggregate insurance penetration, life and non-life insurance penetration to economic growth to support the ‘supply-leading’ hypothesis. The findings have implications for insurance market development in Ghana.

  3. Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

    KAUST Repository

    Yan, Yuan

    2017-11-20

    When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.

  4. PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

    OpenAIRE

    I PUTU YUDI PRABHADIKA; NI KETUT TARI TASTRAWATI; LUH PUTU IDA HARINI

    2018-01-01

    Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indi...

  5. Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil; Hofman, Radek

    2012-01-01

    Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf

  6. A comparison of two least-squared random coefficient autoregressive models: with and without autocorrelated errors

    OpenAIRE

    Autcha Araveeporn

    2013-01-01

    This paper compares a Least-Squared Random Coefficient Autoregressive (RCA) model with a Least-Squared RCA model based on Autocorrelated Errors (RCA-AR). We looked at only the first order models, denoted RCA(1) and RCA(1)-AR(1). The efficiency of the Least-Squared method was checked by applying the models to Brownian motion and Wiener process, and the efficiency followed closely the asymptotic properties of a normal distribution. In a simulation study, we compared the performance of RCA(1) an...

  7. Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

    Science.gov (United States)

    1987-02-04

    U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of

  8. Robust estimation of autoregressive processes using a mixture-based filter-bank

    Czech Academy of Sciences Publication Activity Database

    Šmídl, V.; Anthony, Q.; Kárný, Miroslav; Guy, Tatiana Valentine

    2005-01-01

    Roč. 54, č. 4 (2005), s. 315-323 ISSN 0167-6911 R&D Projects: GA AV ČR IBS1075351; GA ČR GA102/03/0049; GA ČR GP102/03/P010; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian estimation * probabilistic mixtures * recursive estimation Subject RIV: BC - Control Systems Theory Impact factor: 1.239, year: 2005 http://library.utia.cas.cz/separaty/historie/karny-robust estimation of autoregressive processes using a mixture-based filter- bank .pdf

  9. Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

    Science.gov (United States)

    Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

    2015-04-01

    A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built

  10. A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory

    DEFF Research Database (Denmark)

    Nonejad, Nima

    We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte...... Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive...... forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications....

  11. Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

    KAUST Repository

    Yan, Yuan; Genton, Marc G.

    2017-01-01

    When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.

  12. Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions

    DEFF Research Database (Denmark)

    Chon, K H; Cohen, R J; Holstein-Rathlou, N H

    1997-01-01

    A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...

  13. Raster images vectorization system

    OpenAIRE

    Genytė, Jurgita

    2006-01-01

    The problem of raster images vectorization was analyzed and researched in this work. Existing vectorization systems are quite expensive, the results are inaccurate, and the manual vectorization of a large number of drafts is impossible. That‘s why our goal was to design and develop a new raster images vectorization system using our suggested automatic vectorization algorithm and the way to record results in a new universal vectorial file format. The work consists of these main parts: analysis...

  14. Quercetin - A Flavonoid Compound from Sarcopyramis bodinieri var ...

    African Journals Online (AJOL)

    DAPI staining and PARP SDS-PAGE tests showed 60 μM quercetin could induce potential apoptotic activity in HepG2 liver cancer cells. Conclusion: Quercetin was the major cytotoxicity constituent in S. bodinieri var. delicate. Keywords: Apoptotic activity, Quercetin, Sarcopyramis bodinieri var. delicate, HepG2 liver cancer ...

  15. Growth and provenance variation of Pinus caribaea var ...

    African Journals Online (AJOL)

    CAMCORE has visited 33 populations of Pinus caribaea var. hondurensis in Belize, Guatemala, Honduras, El Salvador, Nicaragua, and Quintana Roo, Mexico. Seed collections have been made in 29 provenances from 1, 325 mother trees. A total of 21 provenances and sources of Pinus caribaea var. hondurensis were ...

  16. Larvicidal Activity of Isodon japonicus var. glaucocalyx (Maxim ...

    African Journals Online (AJOL)

    HP

    Purpose: To determine the larvicidal activity of the essential oil derived from Isodon japonicus var. ... Methods: The essential oil of I. japonicus var. glaucocalyx aerial parts was obtained by ..... µg/mL; G. silvatica leaves, LC50 = 117.9 µg/mL.

  17. ESTIMASI NILAI VaR PORTOFOLIO MENGGUNAKAN FUNGSI ARCHIMEDEAN COPULA

    Directory of Open Access Journals (Sweden)

    AULIA ATIKA PRAWIBTA SUHARTO

    2017-01-01

    Full Text Available Value at Risk explains the magnitude of the worst losses occurred in financial products investments with a certain level of confidence and time interval. The purpose of this study is to estimate the VaR of portfolio using Archimedean Copula family. The methods for calculating the VaR are as follows: (1 calculating the stock return; (2 calculating descriptive statistics of return; (3 checking for the nature of autocorrelation and heteroscedasticity effects on stock return data; (4 checking for the presence of extreme value by using Pareto tail; (5 estimating the parameters of Achimedean Copula family; (6 conducting simulations of Archimedean Copula; (7 estimating the value of the stock portfolio VaR. This study uses the closing price of TLKM and GGRM. At 90% the VaR obtained using Clayton, Gumbel, Frank copulas are 0.9562%, 1.0189%, 0.9827% respectively. At 95% the VaR obtained using Clayton, Gumbel, Frank copulas are 1.2930%, 1.2522%, 1.3152% respectively. At 99% the VaR obtained using Clayton, Gumbel, Frank copulas are 2.0327%, 1.9164%, is 1.8678% respectively. In conclusion estimation of VaR using Clayton copula yields the highest VaR.

  18. Een nieuwe naam voor Arenaria serpyllifolia L. var. macrocarpa Lloyd

    NARCIS (Netherlands)

    Gutermann, W.; Mennema, J.

    1983-01-01

    The name Arenaria serpyllifoiia L. var. macrocarpa Lloyd is illegitimate, because of the existence of the earlier non-synonymous A. serpyllifolia0 macrocarpa Godron. As on the level of variety no other name is available, we call the taxon A renaria serpylli/olia L. var. Iloydn (Jord.) Gutermann et

  19. Kochen-Specker vectors

    International Nuclear Information System (INIS)

    Pavicic, Mladen; Merlet, Jean-Pierre; McKay, Brendan; Megill, Norman D

    2005-01-01

    We give a constructive and exhaustive definition of Kochen-Specker (KS) vectors in a Hilbert space of any dimension as well as of all the remaining vectors of the space. KS vectors are elements of any set of orthonormal states, i.e., vectors in an n-dimensional Hilbert space, H n , n≥3, to which it is impossible to assign 1s and 0s in such a way that no two mutually orthogonal vectors from the set are both assigned 1 and that not all mutually orthogonal vectors are assigned 0. Our constructive definition of such KS vectors is based on algorithms that generate MMP diagrams corresponding to blocks of orthogonal vectors in R n , on algorithms that single out those diagrams on which algebraic (0)-(1) states cannot be defined, and on algorithms that solve nonlinear equations describing the orthogonalities of the vectors by means of statistically polynomially complex interval analysis and self-teaching programs. The algorithms are limited neither by the number of dimensions nor by the number of vectors. To demonstrate the power of the algorithms, all four-dimensional KS vector systems containing up to 24 vectors were generated and described, all three-dimensional vector systems containing up to 30 vectors were scanned, and several general properties of KS vectors were found

  20. VaR Methodology Application for Banking Currency Portfolios

    Directory of Open Access Journals (Sweden)

    Daniel Armeanu

    2007-02-01

    Full Text Available VaR has become the standard measure that financial analysts use to quantify market risk. VaR measures can have many applications, such as in risk management, to evaluate the performance of risk takers and for regulatory requirements, and hence it is very important to develop methodologies that provide accurate estimates. In particular, the Basel Committee on Banking Supervision at the Bank for International Settlements imposes to financial institutions such as banks and investment firms to meet capital requirements based on VaR estimates. In this paper we determine VaR for a banking currency portfolio and respect rules of National Bank of Romania regarding VaR report.

  1. Vector regression introduced

    Directory of Open Access Journals (Sweden)

    Mok Tik

    2014-06-01

    Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

  2. Recursive wind speed forecasting based on Hammerstein Auto-Regressive model

    International Nuclear Information System (INIS)

    Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier

    2015-01-01

    Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling

  3. Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

    Science.gov (United States)

    Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

    2010-12-01

    The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

  4. Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

    Directory of Open Access Journals (Sweden)

    Rahul Tripathi

    2014-01-01

    Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

  5. Order-disorder transitions in time-discrete mean field systems with memory: a novel approach via nonlinear autoregressive models

    International Nuclear Information System (INIS)

    Frank, T D; Mongkolsakulvong, S

    2015-01-01

    In a previous study strongly nonlinear autoregressive (SNAR) models have been introduced as a generalization of the widely-used time-discrete autoregressive models that are known to apply both to Markov and non-Markovian systems. In contrast to conventional autoregressive models, SNAR models depend on process mean values. So far, only linear dependences have been studied. We consider the case in which process mean values can have a nonlinear impact on the processes under consideration. It is shown that such models describe Markov and non-Markovian many-body systems with mean field forces that exhibit a nonlinear impact on single subsystems. We exemplify that such nonlinear dependences can describe order-disorder phase transitions of time-discrete Markovian and non-Markovian many-body systems. The relevant order parameter equations are derived and issues of stability and stationarity are studied. (paper)

  6. The effects of the fungus Metarhizium anisopliae var. acridum on different stages of Lutzomyia longipalpis (Diptera: Psychodidae).

    Science.gov (United States)

    Amóra, Sthenia Santos Albano; Bevilaqua, Claudia Maria Leal; Feijó, Francisco Marlon Carneiro; Pereira, Romeika Hermínia de Macedo Assunção; Alves, Nilza Dutra; Freire, Fúlvio Aurélio de Morais; Kamimura, Michel Toth; de Oliveira, Diana Magalhães; Luna-Alves Lima, Elza Aurea; Rocha, Marcos Fábio Gadelha

    2010-03-01

    The control of Visceral Leishmaniasis (VL) vector is often based on the application of chemical residual insecticide. However, this strategy has not been effective. The continuing search for an appropriate vector control may include the use of biological control. This study evaluates the effects of the fungus Metarhizium anisopliae var. acridum on Lutzomyia longipalpis. Five concentrations of the fungus were utilized, 1 x 10(4) to 1 x 10(8) conidia/ml, accompanied by controls. The unhatched eggs, larvae and dead adults previously exposed to fungi were sown to reisolate the fungi and analysis of parameters of growth. The fungus was subsequently identified by PCR and DNA sequencing. M. anisopliae var. acridum reduced egg hatching by 40%. The mortality of infected larvae was significant. The longevity of infected adults was lower than that of negative controls. The effects of fungal infection on the hatching of eggs laid by infected females were also significant. With respect to fungal growth parameters post-infection, only vegetative growth was not significantly higher than that of the fungi before infection. The revalidation of the identification of the reisolated fungus was confirmed post-passage only from adult insects. In terms of larvae mortality and the fecundity of infected females, the results were significant, proving that the main vector species of VL is susceptible to infection by this entomopathogenic fungus in the adult stage. Copyright 2009 Elsevier B.V. All rights reserved.

  7. Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

    Science.gov (United States)

    Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

    2012-12-01

    NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function

  8. Somatic Embryogenesis in Olive (Olea europaea L. subsp. europaea var. sativa and var. sylvestris).

    Science.gov (United States)

    Rugini, Eddo; Silvestri, Cristian

    2016-01-01

    Protocols for olive somatic embryogenesis from zygotic embryos and mature tissues have been described for both Olea europaea sub. europaea var. sativa and var. sylvestris. Immature zygotic embryos (no more than 75 days old), used after fruit collection or stored at 12-14 °C for 2-3 months, are the best responsive explants and very slightly genotype dependent, and one single protocol can be effective for a wide range of genotypes. On the contrary, protocols for mature zygotic embryos and for mature tissue of cultivars are often genotype specific, so that they may require many adjustments according to genotypes. The use of thidiazuron and cefotaxime seems to be an important trigger for induction phase particularly for tissues derived from cultivars. Up to now, however, the application of this technique for large-scale propagation is hampered also by the low rate of embryo germination; it proves nonetheless very useful for genetic improvement.

  9. VectorBase

    Data.gov (United States)

    U.S. Department of Health & Human Services — VectorBase is a Bioinformatics Resource Center for invertebrate vectors. It is one of four Bioinformatics Resource Centers funded by NIAID to provide web-based...

  10. Delice(Olea europea var. oleaster L.) ile zeytin (Olea europea var.sativa) arasında anatomik ve palinojik ayrıcalıklar (The Anatomic And Palynological Differences Between Olea europea var. oleaster L. AND Olea europea var.sativa)

    OpenAIRE

    Kaya, Zafer

    1991-01-01

    Delice(Olea europea var. oleaster L.) ile zeytin (Olea europea var.sativa) arasında anatomik ve palinojik ayrıcalıklar (The Anatomic And Palynological Differences Between Olea europea var. oleaster L. AND Olea europea var.sativa)

  11. Generalization of concurrence vectors

    International Nuclear Information System (INIS)

    Yu Changshui; Song Heshan

    2004-01-01

    In this Letter, based on the generalization of concurrence vectors for bipartite pure state with respect to employing tensor product of generators of the corresponding rotation groups, we generalize concurrence vectors to the case of mixed states; a new criterion of separability of multipartite pure states is given out, for which we define a concurrence vector; we generalize the vector to the case of multipartite mixed state and give out a good measure of free entanglement

  12. Vector Network Coding

    OpenAIRE

    Ebrahimi, Javad; Fragouli, Christina

    2010-01-01

    We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L X L coding matrices that play a similar role as coding coefficients in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector co...

  13. Vector Network Coding Algorithms

    OpenAIRE

    Ebrahimi, Javad; Fragouli, Christina

    2010-01-01

    We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algori...

  14. Chromosome behaviour in Rhoeo spathacea var. variegata.

    Science.gov (United States)

    Lin, Y J

    1980-01-01

    Rhoeo spathacea var. variegata is unusual in that its twelve chromosomes are arranged in a ring at meiosis. The order of the chromosomes has been established, and each chromosome arm has been designated a letter in accordance with the segmental interchange theory. Chromosomes are often irregularly orientated at metaphase I. Chromosomes at anaphase I are generally distributed equally (6-6, 58.75%) although not necessarily balanced. Due to adjacent distribution, 7-5 distribution at anaphase I was frequently observed (24.17%), and due to lagging, 6-1-5 and 5-2-5 distributions were also observed (10.83% and 3.33% respectively). Three types of abnormal distribution, 8-4, 7-1-4 and 6-2-4 were observed very infrequently (2.92% total), and their possible origins are discussed. Irregularities, such as adjacent distribution and lagging, undoubtedly reduce the fertility of the plant because of the resulting unbalanced gametes.

  15. 1D-VAR Retrieval Using Superchannels

    Science.gov (United States)

    Liu, Xu; Zhou, Daniel; Larar, Allen; Smith, William L.; Schluessel, Peter; Mango, Stephen; SaintGermain, Karen

    2008-01-01

    Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.

  16. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  17. Convexity and Marginal Vectors

    NARCIS (Netherlands)

    van Velzen, S.; Hamers, H.J.M.; Norde, H.W.

    2002-01-01

    In this paper we construct sets of marginal vectors of a TU game with the property that if the marginal vectors from these sets are core elements, then the game is convex.This approach leads to new upperbounds on the number of marginal vectors needed to characterize convexity.An other result is that

  18. Custodial vector model

    DEFF Research Database (Denmark)

    Becciolini, Diego; Franzosi, Diogo Buarque; Foadi, Roshan

    2015-01-01

    We analyze the Large Hadron Collider (LHC) phenomenology of heavy vector resonances with a $SU(2)_L\\times SU(2)_R$ spectral global symmetry. This symmetry partially protects the electroweak S-parameter from large contributions of the vector resonances. The resulting custodial vector model spectrum...

  19. Pitfalls in VAR based return decompositions: A clarification

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard; Tanggaard, Carsten

    in their analysis is not "cashflow news" but "inter- est rate news" which should not be zero. Consequently, in contrast to what Chen and Zhao claim, their decomposition does not serve as a valid caution against VAR based decompositions. Second, we point out that in order for VAR based decompositions to be valid......Based on Chen and Zhao's (2009) criticism of VAR based return de- compositions, we explain in detail the various limitations and pitfalls involved in such decompositions. First, we show that Chen and Zhao's interpretation of their excess bond return decomposition is wrong: the residual component...

  20. How can the origin of the decay var-phi →3π be determined?

    International Nuclear Information System (INIS)

    Achasov, N.N.; Kozhevnikov, A.A.

    1992-01-01

    It is shown that the interference pattern in the region √s = 1.05 GeV in the reaction e + e - →ω, var-phi →3π can be used to determine unambiguously whether the decay var-phi →3π occurs because of var-phi ω mixing, var-phi →ω→3π, or because of the direct transition var-phi →3π. 8 refs., 3 figs., 1 tab

  1. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    International Nuclear Information System (INIS)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

    2014-01-01

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval

  2. Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

    Science.gov (United States)

    Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

    2014-10-01

    The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

  3. Packet loss replacement in voip using a recursive low-order autoregressive modelbased speech

    International Nuclear Information System (INIS)

    Miralavi, Seyed Reza; Ghorshi, Seyed; Mortazavi, Mohammad; Choupan, Jeiran

    2011-01-01

    In real-time packet-based communication systems one major problem is misrouted or delayed packets which results in degraded perceived voice quality. When some speech packets are not available on time, the packet is known as lost packet in real-time communication systems. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system in order to avoid quality reduction due to packet loss a suitable method and/or algorithm is needed to replace the missing segments of speech. In this paper, we introduce a recursive low order autoregressive (AR) model for replacement of lost speech segment. The evaluation results show that this method has a lower mean square error (MSE) and low complexity compared to the other efficient methods like high-order AR model without any substantial degradation in perceived voice quality.

  4. A revival of the autoregressive distributed lag model in estimating energy demand relationships

    Energy Technology Data Exchange (ETDEWEB)

    Bentzen, J.; Engsted, T.

    1999-07-01

    The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)

  5. Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Ahsan Shazaib

    2017-01-01

    Full Text Available Gas turbine (GT engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR models to predict remaining useful life (RUL of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT.

  6. Autoregressive techniques for acoustic detection of in-sodium water leaks

    International Nuclear Information System (INIS)

    Hayashi, K.

    1997-01-01

    We have been applied a background signal whitening filter built by univariate autoregressive model to the estimation problem of the leak start time and duration. In the 1995 present benchmark stage, we evaluated the method using acoustic signals from real hydrogen or water/steam injection experiments. The results show that the signal processing technique using this filter can detect reliability the leak signals with a sufficient signal-to-noise ratio. Even if the sensor signal contains non-boiling or non-leak high-amplitude pulses, they can be classified by spectral information. Especially, the feature signal made from the time-frequency spectrum of the filtered signal is very sensitive and useful. (author). 8 refs, 14 figs, 6 tabs

  7. Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

    Science.gov (United States)

    Hamid, Mohd Fahmi Abdul; Shabri, Ani

    2017-05-01

    Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

  8. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  9. Integer valued autoregressive processes with generalized discrete Mittag-Leffler marginals

    Directory of Open Access Journals (Sweden)

    Kanichukattu K. Jose

    2013-05-01

    Full Text Available In this paper we consider a generalization of discrete Mittag-Leffler distributions. We introduce and study the properties of a new distribution called geometric generalized discrete Mittag-Leffler distribution. Autoregressive processes with geometric generalized discrete Mittag-Leffler distributions are developed and studied. The distributions are further extended to develop a more general class of geometric generalized discrete semi-Mittag-Leffler distributions. The processes are extended to higher orders also. An application with respect to an empirical data on customer arrivals in a bank counter is also given. Various areas of potential applications like human resource development, insect growth, epidemic modeling, industrial risk modeling, insurance and actuaries, town planning etc are also discussed.

  10. Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

    International Nuclear Information System (INIS)

    Benmouiza, Khalil; Cheknane, Ali

    2013-01-01

    Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results

  11. Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters

    Directory of Open Access Journals (Sweden)

    V. B. Goryainov

    2017-01-01

    Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the

  12. Autoregressive-model-based missing value estimation for DNA microarray time series data.

    Science.gov (United States)

    Choong, Miew Keen; Charbit, Maurice; Yan, Hong

    2009-01-01

    Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

  13. Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

    DEFF Research Database (Denmark)

    Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K

    1991-01-01

    The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...

  14. Forecasting and simulating wind speed in Corsica by using an autoregressive model

    International Nuclear Information System (INIS)

    Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A.

    2003-01-01

    Alternative approaches for generating wind speed time series are discussed. The method utilized involves the use of an autoregressive process model. The model has been applied to three Mediterranean sites in Corsica and has been used to generate 3-hourly synthetic time series for these considered sites. The synthetic time series have been examined to determine their ability to preserve the statistical properties of the Corsican wind speed time series. In this context, using the main statistical characteristics of the wind speed (mean, variance, probability distribution, autocorrelation function), the data simulated are compared to experimental ones in order to check whether the wind speed behavior was correctly reproduced over the studied periods. The purpose is to create a data generator in order to construct a reference year for wind systems simulation in Corsica

  15. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2012-01-01

    optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....

  16. Investigation of the resonant power oscillation in the Halden Boiling Water Reactor by autoregressive modeling

    International Nuclear Information System (INIS)

    Oguma, Ritsuo

    1980-01-01

    In the HBWR (Halden Boiling Water Reactor), there exists a resonant power oscillation with period about 0.04 Hz at power levels higher than about 9.5 MWt. While the resonant oscillation in not so large as to affect the normal reactor operation, it is significant, from the viewpoint of reactor diagnosis, to grasp its characteristics and find the cause. Noise analysis based on the autoregressive (AR) modeling technique has been made to reveal the driving source for this oscillation which led to the suggestion that it is attributed to the dynamic interference of heat exchange process between two parallel-connected steam transformers against the reactor. The present study demonstrates that the method used here is highly effective for tracing back to a noise source inducing the variation of quantities in a system, and also applicable to problems of reactor noise analysis and diagnosis. (author)

  17. Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

    Science.gov (United States)

    Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

    2017-12-01

    An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

  18. A revival of the autoregressive distributed lag model in estimating energy demand relationships

    Energy Technology Data Exchange (ETDEWEB)

    Bentzen, J; Engsted, T

    1999-07-01

    The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)

  19. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    Science.gov (United States)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

    2014-09-01

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

  20. Medium term municipal solid waste generation prediction by autoregressive integrated moving average

    Energy Technology Data Exchange (ETDEWEB)

    Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)

    2014-09-12

    Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

  1. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  2. Statistical aspects of autoregressive-moving average models in the assessment of radon mitigation

    International Nuclear Information System (INIS)

    Dunn, J.E.; Henschel, D.B.

    1989-01-01

    Radon values, as reflected by hourly scintillation counts, seem dominated by major, pseudo-periodic, random fluctuations. This methodological paper reports a moderate degree of success in modeling these data using relatively simple autoregressive-moving average models to assess the effectiveness of radon mitigation techniques in existing housing. While accounting for the natural correlation of successive observations, familiar summary statistics such as steady state estimates, standard errors, confidence limits, and tests of hypothesis are produced. The Box-Jenkins approach is used throughout. In particular, intervention analysis provides an objective means of assessing the effectiveness of an active mitigation measure, such as a fan off/on cycle. Occasionally, failure to declare a significant intervention has suggested a means of remedial action in the data collection procedure

  3. Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market

    Science.gov (United States)

    Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow

    2017-04-01

    This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.

  4. Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.

    Science.gov (United States)

    Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

    2017-02-15

    Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

    Science.gov (United States)

    Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

    2015-12-01

    There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

  6. Rotations with Rodrigues' vector

    International Nuclear Information System (INIS)

    Pina, E

    2011-01-01

    The rotational dynamics was studied from the point of view of Rodrigues' vector. This vector is defined here by its connection with other forms of parametrization of the rotation matrix. The rotation matrix was expressed in terms of this vector. The angular velocity was computed using the components of Rodrigues' vector as coordinates. It appears to be a fundamental matrix that is used to express the components of the angular velocity, the rotation matrix and the angular momentum vector. The Hamiltonian formalism of rotational dynamics in terms of this vector uses the same matrix. The quantization of the rotational dynamics is performed with simple rules if one uses Rodrigues' vector and similar formal expressions for the quantum operators that mimic the Hamiltonian classical dynamics.

  7. Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

    Science.gov (United States)

    Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

    2016-01-01

    The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…

  8. Activation and clustering of a Plasmodium falciparum var gene are affected by subtelomeric sequences.

    Science.gov (United States)

    Duffy, Michael F; Tang, Jingyi; Sumardy, Fransisca; Nguyen, Hanh H T; Selvarajah, Shamista A; Josling, Gabrielle A; Day, Karen P; Petter, Michaela; Brown, Graham V

    2017-01-01

    The Plasmodium falciparum var multigene family encodes the cytoadhesive, variant antigen PfEMP1. P. falciparum antigenic variation and cytoadhesion specificity are controlled by epigenetic switching between the single, or few, simultaneously expressed var genes. Most var genes are maintained in perinuclear clusters of heterochromatic telomeres. The active var gene(s) occupy a single, perinuclear var expression site. It is unresolved whether the var expression site forms in situ at a telomeric cluster or whether it is an extant compartment to which single chromosomes travel, thus controlling var switching. Here we show that transcription of a var gene did not require decreased colocalisation with clusters of telomeres, supporting var expression site formation in situ. However following recombination within adjacent subtelomeric sequences, the same var gene was persistently activated and did colocalise less with telomeric clusters. Thus, participation in stable, heterochromatic, telomere clusters and var switching are independent but are both affected by subtelomeric sequences. The var expression site colocalised with the euchromatic mark H3K27ac to a greater extent than it did with heterochromatic H3K9me3. H3K27ac was enriched within the active var gene promoter even when the var gene was transiently repressed in mature parasites and thus H3K27ac may contribute to var gene epigenetic memory. © 2016 Federation of European Biochemical Societies.

  9. Insurance penetration and economic growth in Africa: Dynamic effects analysis using Bayesian TVP-VAR approach

    Directory of Open Access Journals (Sweden)

    D.O. Olayungbo

    2016-12-01

    Full Text Available This paper examines the dynamic interactions between insurance and economic growth in eight African countries for the period of 1970–2013. Insurance demand is measured by insurance penetration which accounts for income differences across the sample countries. A Bayesian Time Varying Parameter Vector Auto regression (TVP-VAR model with stochastic volatility is used to analyze the short run and the long run among the variables of interest. Using insurance penetration as a measure of insurance to economic growth, we find positive relationship for Egypt, while short-run negative and long-run positive effects are found for Kenya, Mauritius, and South Africa. On the contrary, negative effects are found for Algeria, Nigeria, Tunisia, and Zimbabwe. Implementation of sound financial reforms and wide insurance coverage are proposed recommendations for insurance development in the selected African countries.

  10. Obtaining of transgenic papaya plants var. Maradol roja that carry out the rice oryzacystatin gene

    Directory of Open Access Journals (Sweden)

    Milady F. Mendoza

    2004-10-01

    Full Text Available Papaya (Carica papaya L., is severely affected by Papaya Ringspot virus, which belongs to plant potyvirus group. A recent strategy for pest control produced by this virus is the transformation with genes encoding cysteine proteinase inhibitors. Rice oryzacistatin gene encoding for cystatins, was inserted in a pCAMBIA binary vector, for genetic transformation of papaya somatic embryos var. Maradol roja, mediated by gene gun. Gene integration was confirmed by means of polimerase chain reaction using the primers designed from gene bar sequence. Forty out of eighty in vitro transgenic papaya lines amplified a 402 fragment which correspond to the expecting size. Key words: Carica papaya, genetic engineering, potyvirus, proteinase inhibitor

  11. VAr reserve concept applied to a wind power plant

    DEFF Research Database (Denmark)

    Martinez, Jorge; Kjær, Philip C.; Rodriguez, Pedro

    2011-01-01

    to wind power plants. This paper proposes two different VAr reserve control strategies for a wind power plant. The amount of dynamic VAr available most of the operation time, makes the wind power plant (WPP) a good candidate to include a VAr reserve management system. Two different ways of implementing...... a VAr management system are proposed and analyzed. Such a reactive power reserve may be provided by the wind power plant since the amount of reactive power installed for most active power working points exceeds the demand required by the grid operator. Basically, this overrated reactive power capacity...... is a consequence of sizing wind turbine facilities for maximum active power level. The reactive power losses, due to active power transportation inside the plant (normally two transformers), and P-Q wind turbine characteristics define the P-Q reserve chart. By utilizing the intrinsic overrated reactive power...

  12. Molt disruption and mortality of Locusta migratoria var. manilensis ...

    African Journals Online (AJOL)

    IGRs) on the oriental migratory locust Locusta migratoria var. manilensis were assessed. Under laboratory conditions, at the highest tested dose rate of 300 ppm, the percent mortality and molt inhibition after two weeks for the five tested ...

  13. ClinVar data parsing [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Xiaolei Zhang

    2017-05-01

    Full Text Available This software repository provides a pipeline for converting raw ClinVar data files into analysis-friendly tab-delimited tables, and also provides these tables for the most recent ClinVar release. Separate tables are generated for genome builds GRCh37 and GRCh38 as well as for mono-allelic variants and complex multi-allelic variants. Additionally, the tables are augmented with allele frequencies from the ExAC and gnomAD datasets as these are often consulted when analyzing ClinVar variants. Overall, this work provides ClinVar data in a format that is easier to work with and can be directly loaded into a variety of popular analysis tools such as R, python pandas, and SQL databases.

  14. Immunogenic Properties of Ricinus Communis Var Minor Seed on ...

    African Journals Online (AJOL)

    Prof. Ogunji

    1College of Health Technology, Zawan, Plateau State.2Department of ... Ricinus communis var minor seed included in their feed (5g/100g body weight). ... White Blood cell Count (WBC) count and lymphocytosis in the differential count.

  15. Survey of cyclopids (Crustacea, Copepoda in Brazil and preliminary screening of their potential as dengue vector predators

    Directory of Open Access Journals (Sweden)

    Santos Luciana Urbano dos

    1997-01-01

    Full Text Available INTRODUCTION: Cyclopid copepods are known to be good mosquito controllers, specially as regards the larvae of the dengue vectors Aedes aegypti and Ae. albopictus. MATERIAL AND METHOD: The objective of the study was to survey the local copepod fauna and search for new strains of M. longisetus var. longisetus, comparing the potential of the samples found with the current strain ML-01 against Ae. albopictus larvae, under laboratory conditions. Eleven bodies of water in Campinas, SP, Brazil, were screened for copepods by collecting 1.5 l of water from each of then. The predatory potential of adults copepods was evaluated over 24 h, in the laboratory, for groups of 5 individuals preying upon 30 first instar Ae. albopictus larvae. RESULTS AND CONCLUSION: The following cyclopid species were found: Metacyclops mendocinus, Tropocyclops prasinus, Eucyclops sp, Eucyclops serrulatus, Eucyclops solitarius, Eucyclops ensifer, Macrocyclops albidus var. albidus and Mesocyclops longisetus var. longisetus. The predatory potential of these copepods ranged from nil to 97.3%. A sample collected in the field containing only M. longisetus var. longisetus showed the best control efficiency with no significant difference from a three-year old laboratory culture (ML-01 of the same species evaluated for comparison. The sample with few M. albidus var. albidus was ranked in second place showing an average 25.9% efficiency. The use of copepods in trap tires as dengue vector controllers is discussed.

  16. Orientation of the Fiscal Policy in Tunisia: Structural VAR Analysis

    Directory of Open Access Journals (Sweden)

    Wissem Khanfir

    2017-06-01

    Full Text Available The objective of this paper is to indicate the orientation of fiscal policy in Tunisia, using the structural budget balance, during the period 1972-2014. For this purpose, we estimate a structural VAR model consisting of the fiscal deficit to current GDP ratio and the volume of economic activity represented by the real GDP. We estimate bivariate structural VAR in order to decompose fiscal deficit fluctuations into different disturbances.

  17. An orientin derivative isolated from Passiflora tripartita var. mollissima

    OpenAIRE

    Ramos, Freddy A.; Castellanos, Leonardo; López, César; Palacios, Lizeth; Duque, Carmenza; Pacheco, Ricardo; Guzmán, Antonio

    2010-01-01

    Passiflora tripartita var. mollisima (banana passion fruit) is an edible fruit widespread in the Andean highlands of Colombia and Ecuador. The fruit is used for juices as well as for the sedative properties of the leaves. As a contribution to the chemical characterization of this species, a new compound, 4'- methoxyluteolin-8-C-6”acetylglucopyranoside, was isolated from the ethanolic extract of Passiflora tripartita var. mollisima leaves and identified by spectroscopical data (NMR, MS, UV).

  18. Da abort ikke var en sag for kvinden

    DEFF Research Database (Denmark)

    Schultz, Annette Østergaard

    2013-01-01

    Case fra tiden før 1973, hvor der ikke var fri abort i Danmark. Sagen er fundet i Mødrehjælpens arkiv og giver som andre sager indblik i kvindens situation. Led i serien: Nyt i arkivet.......Case fra tiden før 1973, hvor der ikke var fri abort i Danmark. Sagen er fundet i Mødrehjælpens arkiv og giver som andre sager indblik i kvindens situation. Led i serien: Nyt i arkivet....

  19. Saccharomyces cerevisiae var. boulardii fungemia following probiotic treatment

    OpenAIRE

    Appel-da-Silva, Marcelo C.; Narvaez, Gabriel A.; Perez, Leandro R.R.; Drehmer, Laura; Lewgoy, Jairo

    2017-01-01

    Probiotics are commonly prescribed as an adjuvant in the treatment of antibiotic-associated diarrhea caused by Clostridium difficile. We report the case of an immunocompromised 73-year-old patient on chemotherapy who developed Saccharomyces cerevisiae var. boulardii fungemia in a central venous catheter during treatment of antibiotic-associated pseudomembranous colitis with the probiotic Saccharomyces cerevisiae var. boulardii. Fungemia was resolved after interruption of probiotic administrat...

  20. VAR Methodology Used for Exchange Risk Measurement and Prevention

    Directory of Open Access Journals (Sweden)

    Florentina Balu

    2006-05-01

    Full Text Available In this article we discuss one of the modern risk measuring techniques Value-at-Risk (VaR. Currently central banks in major money centers, under the auspices of the BIS Basle Committee, adopt the VaR system to evaluate the market risk of their supervised banks. Banks regulators ask all commercial banks to report VaRs with their internal models. Value at risk (VaR is a powerful tool for assessing market risk, but it also imposes a challenge. Its power is its generality. Unlike market risk metrics such as the Greeks, duration and convexity, or beta, which are applicable to only certain asset categories or certain sources of market risk, VaR is general. It is based on the probability distribution for a portfolio’s market value. Value at Risk (VAR calculates the maximum loss expected (or worst case scenario on an investment, over a given time period and given a specified degree of confidence. There are three methods by which VaR can be calculated: the historical simulation, the variance-covariance method and the Monte Carlo simulation. The variance-covariance method is easiest because you need to estimate only two factors: average return and standard deviation. However, it assumes returns are well-behaved according to the symmetrical normal curve and that historical patterns will repeat into the future. The historical simulation improves on the accuracy of the VAR calculation, but requires more computational data; it also assumes that “past is prologue”. The Monte Carlo simulation is complex, but has the advantage of allowing users to tailor ideas about future patterns that depart from historical patterns.

  1. VAR Methodology Used for Exchange Risk Measurement and Prevention

    Directory of Open Access Journals (Sweden)

    Ion Stancu

    2006-03-01

    Full Text Available In this article we discuss one of the modern risk measuring techniques Value-at-Risk (VaR. Currently central banks in major money centers, under the auspices of the BIS Basle Committee, adopt the VaR system to evaluate the market risk of their supervised banks. Banks regulators ask all commercial banks to report VaRs with their internal models. Value at risk (VaR is a powerful tool for assessing market risk, but it also imposes a challenge. Its power is its generality. Unlike market risk metrics such as the Greeks, duration and convexity, or beta, which are applicable to only certain asset categories or certain sources of market risk, VaR is general. It is based on the probability distribution for a portfolio’s market value. Value at Risk (VAR calculates the maximum loss expected (or worst case scenario on an investment, over a given time period and given a specified degree of confidence. There are three methods by which VaR can be calculated: the historical simulation, the variance-covariance method and the Monte Carlo simulation. The variance-covariance method is easiest because you need to estimate only two factors: average return and standard deviation. However, it assumes returns are well-behaved according to the symmetrical normal curve and that historical patterns will repeat into the future. The historical simulation improves on the accuracy of the VAR calculation, but requires more computational data; it also assumes that “past is prologue”. The Monte Carlo simulation is complex, but has the advantage of allowing users to tailor ideas about future patterns that depart from historical patterns.

  2. Genetic analisys of a cross of gaillon (Brassica oleracea var. alboglabra) with cauliflower (B.oleracea var. botrytis)

    OpenAIRE

    Spini, Vanessa B.M.G.; Kerr, Warwick Estevam

    2000-01-01

    The cauliflower (Brassica oleracea var. botrytis) is an annual vegetable cultivated in Southern and Southwestern Brazil with limited production in the Northeast and Centralwest. A variety of Chinese kale, "kaai laan" or "gaillon" (Brassica oleracea var. alboglabra), produces seeds at high temperatures and therefore can do so in North and Northeastern Brazil. Gaillon and cauliflower were crossed 55 times using 10 gaillon plants as mothers and 4 cauliflower plants as pollen donors. From these c...

  3. Supergravity inspired vector curvaton

    International Nuclear Information System (INIS)

    Dimopoulos, Konstantinos

    2007-01-01

    It is investigated whether a massive Abelian vector field, whose gauge kinetic function is growing during inflation, can be responsible for the generation of the curvature perturbation in the Universe. Particle production is studied and it is shown that the vector field can obtain a scale-invariant superhorizon spectrum of perturbations with a reasonable choice of kinetic function. After inflation the vector field begins coherent oscillations, during which it corresponds to pressureless isotropic matter. When the vector field dominates the Universe, its perturbations give rise to the observed curvature perturbation following the curvaton scenario. It is found that this is possible if, after the end of inflation, the mass of the vector field increases at a phase transition at temperature of order 1 TeV or lower. Inhomogeneous reheating, whereby the vector field modulates the decay rate of the inflaton, is also studied

  4. Custodial vector model

    Science.gov (United States)

    Becciolini, Diego; Franzosi, Diogo Buarque; Foadi, Roshan; Frandsen, Mads T.; Hapola, Tuomas; Sannino, Francesco

    2015-07-01

    We analyze the Large Hadron Collider (LHC) phenomenology of heavy vector resonances with a S U (2 )L×S U (2 )R spectral global symmetry. This symmetry partially protects the electroweak S parameter from large contributions of the vector resonances. The resulting custodial vector model spectrum and interactions with the standard model fields lead to distinct signatures at the LHC in the diboson, dilepton, and associated Higgs channels.

  5. Vector Differential Calculus

    OpenAIRE

    HITZER, Eckhard MS

    2002-01-01

    This paper treats the fundamentals of the vector differential calculus part of universal geometric calculus. Geometric calculus simplifies and unifies the structure and notation of mathematics for all of science and engineering, and for technological applications. In order to make the treatment self-contained, I first compile all important geometric algebra relationships,which are necesssary for vector differential calculus. Then differentiation by vectors is introduced and a host of major ve...

  6. Implicit Real Vector Automata

    Directory of Open Access Journals (Sweden)

    Jean-François Degbomont

    2010-10-01

    Full Text Available This paper addresses the symbolic representation of non-convex real polyhedra, i.e., sets of real vectors satisfying arbitrary Boolean combinations of linear constraints. We develop an original data structure for representing such sets, based on an implicit and concise encoding of a known structure, the Real Vector Automaton. The resulting formalism provides a canonical representation of polyhedra, is closed under Boolean operators, and admits an efficient decision procedure for testing the membership of a vector.

  7. Vectorized Monte Carlo

    International Nuclear Information System (INIS)

    Brown, F.B.

    1981-01-01

    Examination of the global algorithms and local kernels of conventional general-purpose Monte Carlo codes shows that multigroup Monte Carlo methods have sufficient structure to permit efficient vectorization. A structured multigroup Monte Carlo algorithm for vector computers is developed in which many particle events are treated at once on a cell-by-cell basis. Vectorization of kernels for tracking and variance reduction is described, and a new method for discrete sampling is developed to facilitate the vectorization of collision analysis. To demonstrate the potential of the new method, a vectorized Monte Carlo code for multigroup radiation transport analysis was developed. This code incorporates many features of conventional general-purpose production codes, including general geometry, splitting and Russian roulette, survival biasing, variance estimation via batching, a number of cutoffs, and generalized tallies of collision, tracklength, and surface crossing estimators with response functions. Predictions of vectorized performance characteristics for the CYBER-205 were made using emulated coding and a dynamic model of vector instruction timing. Computation rates were examined for a variety of test problems to determine sensitivities to batch size and vector lengths. Significant speedups are predicted for even a few hundred particles per batch, and asymptotic speedups by about 40 over equivalent Amdahl 470V/8 scalar codes arepredicted for a few thousand particles per batch. The principal conclusion is that vectorization of a general-purpose multigroup Monte Carlo code is well worth the significant effort required for stylized coding and major algorithmic changes

  8. Vectors and their applications

    CERN Document Server

    Pettofrezzo, Anthony J

    2005-01-01

    Geared toward undergraduate students, this text illustrates the use of vectors as a mathematical tool in plane synthetic geometry, plane and spherical trigonometry, and analytic geometry of two- and three-dimensional space. Its rigorous development includes a complete treatment of the algebra of vectors in the first two chapters.Among the text's outstanding features are numbered definitions and theorems in the development of vector algebra, which appear in italics for easy reference. Most of the theorems include proofs, and coordinate position vectors receive an in-depth treatment. Key concept

  9. Symbolic computer vector analysis

    Science.gov (United States)

    Stoutemyer, D. R.

    1977-01-01

    A MACSYMA program is described which performs symbolic vector algebra and vector calculus. The program can combine and simplify symbolic expressions including dot products and cross products, together with the gradient, divergence, curl, and Laplacian operators. The distribution of these operators over sums or products is under user control, as are various other expansions, including expansion into components in any specific orthogonal coordinate system. There is also a capability for deriving the scalar or vector potential of a vector field. Examples include derivation of the partial differential equations describing fluid flow and magnetohydrodynamics, for 12 different classic orthogonal curvilinear coordinate systems.

  10. An Examination of the Relationship between Financial Development ...

    African Journals Online (AJOL)

    Nneka Umera-Okeke

    common stochastic trend driving their relationship. ... growth models have explained that the interaction between financial development and ..... This study adopted a dynamic vector autoregressive regression (VAR) which explores ... adjustment parameters in the vector error correction model and each column of is a.

  11. Nonlinear dynamic interrelationships between real activity and stock returns

    DEFF Research Database (Denmark)

    Lanne, Markku; Nyberg, Henri

    We explore the differences between the causal and noncausal vector autoregressive (VAR) models in capturing the real activity-stock return-relationship. Unlike the conventional linear VAR model, the noncausal VAR model is capable of accommodating various nonlinear characteristics of the data....... In quarterly U.S. data, we find strong evidence in favor of noncausality, and the best causal and noncausal VAR models imply quite different dynamics. In particular, the linear VAR model appears to underestimate the importance of the stock return shock for the real activity, and the real activity shock...

  12. On the link between oil price and exchange rate: A time-varying VAR parameter approach

    International Nuclear Information System (INIS)

    Bremond, Vincent; Razafindrabe, Tovonony; Hache, Emmanuel

    2015-07-01

    The aim of this paper is to study the relationship between the effective exchange rate of the dollar and the oil price dynamics from 1976 to 2013. In this context, we propose to explore the economic literature dedicated to financial channels factors (exchange rate, monetary policy, and international liquidity) that could affect the oil price dynamics. In addition to oil prices and the effective exchange rate of the dollar, we use the dry cargo index as a proxy for the real economic activity and prices for precious and industrial raw materials. Using a Bayesian time-varying parameter vector auto-regressive estimation, our main results show that the US Dollar effective exchange rate elasticity of the crude oil prices is not constant across the time and remains negative from 1989. It then highlights that a depreciation of the effective exchange rate of the dollar leads to an increase of the crude oil prices. Our paper also demonstrates the growing influence of financial and commodities markets development upon the global economy. (authors)

  13. Vector-Vector Scattering on the Lattice

    Science.gov (United States)

    Romero-López, Fernando; Urbach, Carsten; Rusetsky, Akaki

    2018-03-01

    In this work we present an extension of the LüScher formalism to include the interaction of particles with spin, focusing on the scattering of two vector particles. The derived formalism will be applied to Scalar QED in the Higgs Phase, where the U(1) gauge boson acquires mass.

  14. Selection vector filter framework

    Science.gov (United States)

    Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

    2003-10-01

    We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

  15. Brane vector phenomenology

    International Nuclear Information System (INIS)

    Clark, T.E.; Love, S.T.; Nitta, Muneto; Veldhuis, T. ter; Xiong, C.

    2009-01-01

    Local oscillations of the brane world are manifested as massive vector fields. Their coupling to the Standard Model can be obtained using the method of nonlinear realizations of the spontaneously broken higher-dimensional space-time symmetries, and to an extent, are model independent. Phenomenological limits on these vector field parameters are obtained using LEP collider data and dark matter constraints

  16. Complex Polynomial Vector Fields

    DEFF Research Database (Denmark)

    The two branches of dynamical systems, continuous and discrete, correspond to the study of differential equations (vector fields) and iteration of mappings respectively. In holomorphic dynamics, the systems studied are restricted to those described by holomorphic (complex analytic) functions...... or meromorphic (allowing poles as singularities) functions. There already exists a well-developed theory for iterative holomorphic dynamical systems, and successful relations found between iteration theory and flows of vector fields have been one of the main motivations for the recent interest in holomorphic...... vector fields. Since the class of complex polynomial vector fields in the plane is natural to consider, it is remarkable that its study has only begun very recently. There are numerous fundamental questions that are still open, both in the general classification of these vector fields, the decomposition...

  17. Complex Polynomial Vector Fields

    DEFF Research Database (Denmark)

    Dias, Kealey

    vector fields. Since the class of complex polynomial vector fields in the plane is natural to consider, it is remarkable that its study has only begun very recently. There are numerous fundamental questions that are still open, both in the general classification of these vector fields, the decomposition...... of parameter spaces into structurally stable domains, and a description of the bifurcations. For this reason, the talk will focus on these questions for complex polynomial vector fields.......The two branches of dynamical systems, continuous and discrete, correspond to the study of differential equations (vector fields) and iteration of mappings respectively. In holomorphic dynamics, the systems studied are restricted to those described by holomorphic (complex analytic) functions...

  18. Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

    Science.gov (United States)

    Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

    2017-08-01

    Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.

  19. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    Energy Technology Data Exchange (ETDEWEB)

    Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.

  20. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)

  1. Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects: An autoregressive spectral analysis

    International Nuclear Information System (INIS)

    Yi Guo-Sheng; Wang Jiang; Deng Bin; Wei Xi-Le; Han Chun-Xiao

    2013-01-01

    To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz–4 Hz), theta (4 Hz–8 Hz), alpha (8 Hz–13 Hz), and beta (13 Hz–30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized. (interdisciplinary physics and related areas of science and technology)

  2. Assessment and prediction of air quality using fuzzy logic and autoregressive models

    Science.gov (United States)

    Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

    2012-12-01

    In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

  3. Autoregressive moving average fitting for real standard deviation in Monte Carlo power distribution calculation

    International Nuclear Information System (INIS)

    Ueki, Taro

    2010-01-01

    The noise propagation of tallies in the Monte Carlo power method can be represented by the autoregressive moving average process of orders p and p-1 (ARMA(p,p-1)], where p is an integer larger than or equal to two. The formula of the autocorrelation of ARMA(p,q), p≥q+1, indicates that ARMA(3,2) fitting is equivalent to lumping the eigenmodes of fluctuation propagation in three modes such as the slow, intermediate and fast attenuation modes. Therefore, ARMA(3,2) fitting was applied to the real standard deviation estimation of fuel assemblies at particular heights. The numerical results show that straightforward ARMA(3,2) fitting is promising but a stability issue must be resolved toward the incorporation in the distributed version of production Monte Carlo codes. The same numerical results reveal that the average performance of ARMA(3,2) fitting is equivalent to that of the batch method in MCNP with a batch size larger than one hundred and smaller than two hundred cycles for a 1100 MWe pressurized water reactor. The bias correction of low lag autocovariances in MVP/GMVP is demonstrated to have the potential of improving the average performance of ARMA(3,2) fitting. (author)

  4. Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

    Science.gov (United States)

    Wen, Yalu; Shen, Xiaoxi; Lu, Qing

    2018-05-31

    With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

  5. A Ramp Cosine Cepstrum Model for the Parameter Estimation of Autoregressive Systems at Low SNR

    Directory of Open Access Journals (Sweden)

    Zhu Wei-Ping

    2010-01-01

    Full Text Available A new cosine cepstrum model-based scheme is presented for the parameter estimation of a minimum-phase autoregressive (AR system under low levels of signal-to-noise ratio (SNR. A ramp cosine cepstrum (RCC model for the one-sided autocorrelation function (OSACF of an AR signal is first proposed by considering both white noise and periodic impulse-train excitations. Using the RCC model, a residue-based least-squares optimization technique that guarantees the stability of the system is then presented in order to estimate the AR parameters from noisy output observations. For the purpose of implementation, the discrete cosine transform, which can efficiently handle the phase unwrapping problem and offer computational advantages as compared to the discrete Fourier transform, is employed. From extensive experimentations on AR systems of different orders, it is shown that the proposed method is capable of estimating parameters accurately and consistently in comparison to some of the existing methods for the SNR levels as low as −5 dB. As a practical application of the proposed technique, simulation results are also provided for the identification of a human vocal tract system using noise-corrupted natural speech signals demonstrating a superior estimation performance in terms of the power spectral density of the synthesized speech signals.

  6. Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling

    International Nuclear Information System (INIS)

    Oguma, Ritsuo; Matsubara, Kunihiko

    1980-01-01

    The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)

  7. Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model

    International Nuclear Information System (INIS)

    Hayashi, K.; Shimazaki, J.; Shinohara, Y.

    1996-01-01

    The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)

  8. Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

    Directory of Open Access Journals (Sweden)

    Zhihua Wang

    2014-01-01

    Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.

  9. A method simulating random magnetic field in interplanetary space by an autoregressive method

    International Nuclear Information System (INIS)

    Kato, Masahito; Sakai, Takasuke

    1985-01-01

    With an autoregressive method, we tried to generate the random noise fitting in with the power spectrum which can be analytically Fouriertransformed into an autocorrelation function. Although we can not directly compare our method with FFT by Owens (1978), we can only point out the following; FFT method should determine at first the number of data points N, or the total length to be generated and we cannot generate random data more than N. Because, beyond the NΔy, the generated data repeats the same pattern as below NΔy, where Δy = minimum interval for random noise. So if you want to change or increase N after generating the random noise, you should start the generation from the first step. The characteristic of the generated random number may depend upon the number of N, judging from the generating method. Once the prediction error filters are determined, our method can produce successively the random numbers, that is, we can possibly extend N to infinite without any effort. (author)

  10. Non-linear auto-regressive models for cross-frequency coupling in neural time series

    Science.gov (United States)

    Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

    2017-01-01

    We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

  11. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

    Energy Technology Data Exchange (ETDEWEB)

    Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)

    2008-09-15

    This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)

  12. An Autoregressive and Distributed Lag Model Approach to Inflation in Nigeria

    Directory of Open Access Journals (Sweden)

    Chimere Okechukwu Iheonu

    2017-03-01

    Full Text Available This study scrutinized the precursors of Inflation in Nigeria between the periods 1980 to 2014. The Augmented Dickey-Fuller test was engaged to test for stationarity of the variables while the Autoregressive and Distributed lag (ARDL Model was applied to capture the affiliation between inflation and selected macroeconomic variables. Our findings revealed that there exists a long run relationship between Inflation, money supply, interest rate, GDP per capita and exchange rate in Nigeria while in the short run, money supply has a significant positive one period lag effect on Inflation and Interest Rate also has a significant negative one period lag influence on Inflation in Nigeria. Recommendations are that in the short run, monetary policies should be geared towards the control of money supply and interest rate in Nigeria in other to regulate Inflation and also, the Nigerian economy can afford to vary any of human capital development or technological advancement to boost productivity without causing inflation as GDP per capita proved insignificant in the short run.

  13. A time series model: First-order integer-valued autoregressive (INAR(1))

    Science.gov (United States)

    Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

    2017-07-01

    Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

  14. Self-organising mixture autoregressive model for non-stationary time series modelling.

    Science.gov (United States)

    Ni, He; Yin, Hujun

    2008-12-01

    Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

  15. Evaluation of the autoregression time-series model for analysis of a noisy signal

    International Nuclear Information System (INIS)

    Allen, J.W.

    1977-01-01

    The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)

  16. Forecast of sea surface temperature off the Peruvian coast using an autoregressive integrated moving average model

    Directory of Open Access Journals (Sweden)

    Carlos Quispe

    2013-04-01

    Full Text Available El Niño connects globally climate, ecosystems and socio-economic activities. Since 1980 this event has been tried to be predicted, but until now the statistical and dynamical models are insuffi cient. Thus, the objective of the present work was to explore using an autoregressive moving average model the effect of El Niño over the sea surface temperature (TSM off the Peruvian coast. The work involved 5 stages: identifi cation, estimation, diagnostic checking, forecasting and validation. Simple and partial autocorrelation functions (FAC and FACP were used to identify and reformulate the orders of the model parameters, as well as Akaike information criterium (AIC and Schwarz criterium (SC for the selection of the best models during the diagnostic checking. Among the main results the models ARIMA(12,0,11 were proposed, which simulated monthly conditions in agreement with the observed conditions off the Peruvian coast: cold conditions at the end of 2004, and neutral conditions at the beginning of 2005.

  17. Forecasting Nord Pool day-ahead prices with an autoregressive model

    International Nuclear Information System (INIS)

    Kristiansen, Tarjei

    2012-01-01

    This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.

  18. Autoregressive harmonic analysis of the earth's polar motion using homogeneous international latitude service data

    Science.gov (United States)

    Fong Chao, B.

    1983-12-01

    The homogeneous set of 80-year-long (1900-1979) International Latitude Service (ILS) polar motion data is analyzed using the autoregressive method (Chao and Gilbert, 1980) which resolves and produces estimates for the complex frequency (or frequency and Q) and complex amplitude (or amplitude and phase) of each harmonic component in the data. Principal conclusion of this analysis are that (1) the ILS data support the multiple-component hypothesis of the Chandler wobble (it is found that the Chandler wobble can be adequately modeled as a linear combination of four (coherent) harmonic components, each of which represents a steady, nearly circular, prograte motion, a behavior that is inconsistent with the hypothesis of a single Chandler period excited in a temporally and/or spatially random fashion). (2) the four-component Chandler wobble model ``explains'' the apparent phase reversal during 1920-1940 and the pre-1950 empirical period-amplitude relation, (3) the annual wobble is shown to be rather stationary over the years both in amplitude and in phase and no evidence is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to be marginally retrograde and appears to have a complicated behavior which cannot be resolved because of the shortness of the data set.

  19. Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

    Science.gov (United States)

    Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

    2015-11-01

    There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

  20. Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Chengcheng Xu

    2017-08-01

    Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.

  1. Inflation, Exchange Rates and Interest Rates in Ghana: an Autoregressive Distributed Lag Model

    Directory of Open Access Journals (Sweden)

    Dennis Nchor

    2015-01-01

    Full Text Available This paper investigates the impact of exchange rate movement and the nominal interest rate on inflation in Ghana. It also looks at the presence of the Fisher Effect and the International Fisher Effect scenarios. It makes use of an autoregressive distributed lag model and an unrestricted error correction model. Ordinary Least Squares regression methods were also employed to determine the presence of the Fischer Effect and the International Fisher Effect. The results from the study show that in the short run a percentage point increase in the level of depreciation of the Ghana cedi leads to an increase in the rate of inflation by 0.20%. A percentage point increase in the level of nominal interest rates however results in a decrease in inflation by 0.98%. Inflation increases by 1.33% for every percentage point increase in the nominal interest rate in the long run. An increase in inflation on the other hand increases the nominal interest rate by 0.51% which demonstrates the partial Fisher effect. A 1% increase in the interest rate differential leads to a depreciation of the Ghana cedi by approximately 1% which indicates the full International Fisher effect.

  2. Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX.

    Science.gov (United States)

    Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching

    2016-01-01

    High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.

  3. The Measurement of the Relationship between Taiwan’s Bond Funds’ Net Flow and the Investment Risk -Threshold Autoregressive Model

    OpenAIRE

    Wo-Chiang Lee; Joe-Ming Lee

    2014-01-01

    This article applies the threshold autoregressive model to investigate the relationship between bond funds’ net flow and investment risk in Taiwan. Our empirical findings show that bond funds’ investors are concerned about the investment return and neglect the investment risk. In particular, when expanding the size of the bond funds, fund investors believe that the fund cannot lose any money on investment products. In order to satisfy investors, bond fund managers only target short-term retur...

  4. Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

    Science.gov (United States)

    Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

    2017-01-01

    Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

  5. On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets

    International Nuclear Information System (INIS)

    Benth, Fred Espen; Taib, Che Mohd Imran Che

    2013-01-01

    We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model

  6. To the issue of increasing efficiency of VAR compensation

    Directory of Open Access Journals (Sweden)

    Світлана Костянтинівна Поднебенна

    2015-11-01

    Full Text Available This article describes the features of VAR compensation of variable loads. One of the most common non-symmetric non-linear power consumers are welding power sources. Time and duration of the work of these sources vary randomly. To compensate the consumption of reactive power on the basis of consumption data from the three-phase electricity meters is inefficient. Compensation devices power should be calculated taking into account the asymmetrical consumption/generation of reactive power per phase and changing consumption patterns. Thyristor-controlled reactor, thyristor-switched capacitors, hybrid VAR compensators, active compensators (STATCOMs, «dynamic capacitors» can be used as the VAR compensation devices. Thyristor-controlled reactors can provide smooth regulation of reactive power, but they have high weight and size parameters and are additional sources of higher harmonics. Thyristor-switched capacitors provide stepwise adjustment of reactive power and are subject to the current higher harmonics. Hybrid VAR compensators make it possible to isolate capacitors from the higher harmonics and ensure smooth regulation, which is achieved by active filter introduction to the reactive power compensation devices based on thyristor-switched capacitors. However, this increases the cost of a compensator and complicates its control system. STATCOMs provide smooth regulation of reactive power, but they are too expensive.Perspective direction in the development of effective VAR compensation devices is «dynamic capacitor». As a result of a feasibility study the prospects for further research of electrical grids power efficiency through the development of effective devices for VAR compensation have been established

  7. New prediction for the direct CP-violating parameter var-epsilon prime/var-epsilon and the ΔI=1/2 rule

    International Nuclear Information System (INIS)

    Wu, Yue-Liang

    2001-01-01

    The low-energy dynamics of QCD is investigated with special attention paid to the matching between QCD and chiral perturbation theory (ChPT), and also to some useful algebraic chiral operator relations which survive even when we include chiral loop corrections. It then allows us to evaluate the hadronic matrix elements below the energy scale Λ χ ≅1GeV. Based on the new analyses, we present a consistent prediction for both the direct CP-violating parameter var-epsilonprime/var-epsilon and the ΔI=1/2 rule in kaon decays. In the leading 1/N c approximation, the isospin amplitudes A 0 and A 2 are found to agree well with the data, and the direct CP-violating parameter var-epsilonprime/var-epsilon is predicted to be large, which also confirms our earlier conclusion. Its numerical value is var-epsilonprime/var-epsilon=23.6 -7.8 +12.4 x10 -4 (Imλ t /= 1.2x10 -4 ) which is no longer sensitive to the strange quark mass due to the matching conditions. Taking into account a simultaneous consistent analysis on the isospin amplitudes A 0 and A 2 , the ratio var-epsilonprime/var-epsilon is in favor of the values var-epsilonprime/var-epsilon=(20±9)x10 -4

  8. Fractal vector optical fields.

    Science.gov (United States)

    Pan, Yue; Gao, Xu-Zhen; Cai, Meng-Qiang; Zhang, Guan-Lin; Li, Yongnan; Tu, Chenghou; Wang, Hui-Tian

    2016-07-15

    We introduce the concept of a fractal, which provides an alternative approach for flexibly engineering the optical fields and their focal fields. We propose, design, and create a new family of optical fields-fractal vector optical fields, which build a bridge between the fractal and vector optical fields. The fractal vector optical fields have polarization states exhibiting fractal geometry, and may also involve the phase and/or amplitude simultaneously. The results reveal that the focal fields exhibit self-similarity, and the hierarchy of the fractal has the "weeding" role. The fractal can be used to engineer the focal field.

  9. Saccharomyces cerevisiae var. boulardii fungemia following probiotic treatment

    Directory of Open Access Journals (Sweden)

    Marcelo C. Appel-da-Silva

    2017-12-01

    Full Text Available Probiotics are commonly prescribed as an adjuvant in the treatment of antibiotic-associated diarrhea caused by Clostridium difficile. We report the case of an immunocompromised 73-year-old patient on chemotherapy who developed Saccharomyces cerevisiae var. boulardii fungemia in a central venous catheter during treatment of antibiotic-associated pseudomembranous colitis with the probiotic Saccharomyces cerevisiae var. boulardii. Fungemia was resolved after interruption of probiotic administration without the need to replace the central venous line. Keywords: Saccharomyces, Probiotics, Fungemia, Critical illness, Clostridium difficile

  10. The Vibrio cholerae var regulon encodes a metallo-β-lactamase and an antibiotic efflux pump, which are regulated by VarR, a LysR-type transcription factor.

    Directory of Open Access Journals (Sweden)

    Hong-Ting Victor Lin

    Full Text Available The genome sequence of V. cholerae O1 Biovar Eltor strain N16961 has revealed a putative antibiotic resistance (var regulon that is predicted to encode a transcriptional activator (VarR, which is divergently transcribed relative to the putative resistance genes for both a metallo-β-lactamase (VarG and an antibiotic efflux-pump (VarABCDEF. We sought to test whether these genes could confer antibiotic resistance and are organised as a regulon under the control of VarR. VarG was overexpressed and purified and shown to have β-lactamase activity against penicillins, cephalosporins and carbapenems, having the highest activity against meropenem. The expression of VarABCDEF in the Escherichia coli (ΔacrAB strain KAM3 conferred resistance to a range of drugs, but most significant resistance was to the macrolide spiramycin. A gel-shift analysis was used to determine if VarR bound to the promoter regions of the resistance genes. Consistent with the regulation of these resistance genes, VarR binds to three distinct intergenic regions, varRG, varGA and varBC located upstream and adjacent to varG, varA and varC, respectively. VarR can act as a repressor at the varRG promoter region; whilst this repression was relieved upon addition of β-lactams, these did not dissociate the VarR/varRG-DNA complex, indicating that the de-repression of varR by β-lactams is indirect. Considering that the genomic arrangement of VarR-VarG is strikingly similar to that of AmpR-AmpC system, it is possible that V. cholerae has evolved a system for resistance to the newer β-lactams that would prove more beneficial to the bacterium in light of current selective pressures.

  11. 21 CFR 173.145 - Alpha-Galactosidase derived from Mortierella vinaceae var. raffinoseutilizer.

    Science.gov (United States)

    2010-04-01

    ... vinaceae var. raffinoseutilizer. 173.145 Section 173.145 Food and Drugs FOOD AND DRUG ADMINISTRATION... Alpha-Galactosidase derived from Mortierella vinaceae var. raffinoseutilizer. The food additive alpha-galactosidase and parent mycelial microorganism Mortierella vinaceae var. raffinoseutilizer may be safely used...

  12. Morphological and genetic differences between Coptis japonica var. anemonifolia H. Ohba and Coptis japonica var. major Satake in Hokuriku area.

    Science.gov (United States)

    Kitamura, Masashi; Ando, Hirokazu; Sasaki, Yohei

    2018-03-01

    Coptis japonica is widely distributed in Japan, and its dried rhizome is a source of the domestic herbal medicine Coptidis Rhizoma ( Oren). There are three varieties of C. japonica, two of which, namely, C. japonica var. anemonifolia and C. japonica var. major, are important as sources of traditional medicines. Coptis japonica var. anemonifolia and C. japonica var. major are distinguishable on the basis of their ternate or biternate compound leaves, respectively. In the Hokuriku area, where both C. japonica var. anemonifolia and C. japonica var. major grow naturally, some individual plants cannot be identified unambiguously on the basis of leaf morphology because changes in leaf morphology may occur due to intra-variety variation or crossbreeding between the two varieties. In addition, genetic differences between the two varieties have remained unclear. In this study, we employed new genetic and morphological classification approaches to discriminate between the two varieties. Based on the single nucleotide polymorphisms of the tetrahydroberberine oxidase gene, we found four conserved SNPs between the two varieties and were able to classify C. japonica into two varieties and crossbreeds. Furthermore, we introduced a new leaf type index based on the overall degree of leaflet dissection calculated by surface area of a leaflet and length of leaflet margin and petiolule. Using our new index we were able to discriminate between the two varieties and their crossbreeds more accurately than is possible with the conventional discrimination method. Our genetic and morphological classification methods may be used as novel benchmarks to discriminate between the two varieties and their crossbreeds.

  13. Understanding Vector Fields.

    Science.gov (United States)

    Curjel, C. R.

    1990-01-01

    Presented are activities that help students understand the idea of a vector field. Included are definitions, flow lines, tangential and normal components along curves, flux and work, field conservation, and differential equations. (KR)

  14. GAP Land Cover - Vector

    Data.gov (United States)

    Minnesota Department of Natural Resources — This vector dataset is a detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of combined two-season pairs of...

  15. Sesquilinear uniform vector integral

    Indian Academy of Sciences (India)

    theory, together with his integral, dominate contemporary mathematics. ... directions belonging to Bartle and Dinculeanu (see [1], [6], [7] and [2]). ... in this manner, namely he integrated vector functions with respect to measures of bounded.

  16. Tagged Vector Contour (TVC)

    Data.gov (United States)

    Kansas Data Access and Support Center — The Kansas Tagged Vector Contour (TVC) dataset consists of digitized contours from the 7.5 minute topographic quadrangle maps. Coverage for the state is incomplete....

  17. Vector hysteresis models

    Czech Academy of Sciences Publication Activity Database

    Krejčí, Pavel

    1991-01-01

    Roč. 2, - (1991), s. 281-292 ISSN 0956-7925 Keywords : vector hysteresis operator * hysteresis potential * differential inequality Subject RIV: BA - General Mathematics http://www.math.cas.cz/~krejci/b15p.pdf

  18. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  19. Exotic composite vector boson

    International Nuclear Information System (INIS)

    Akama, K.; Hattori, T.; Yasue, M.

    1991-01-01

    An exotic composite vector boson V is introduced in two dynamical models of composite quarks, leptons, W, and Z. One is based on four-Fermi interactions, in which composite vector bosons are regarded as fermion-antifermion bound states and the other is based on the confining SU(2) L gauge model, in which they are given by scalar-antiscalar bound states. Both approaches describe the same effective interactions for the sector of composite quarks, leptons, W, Z, γ, and V

  20. Vector borne diseases

    OpenAIRE

    Melillo Fenech, Tanya

    2010-01-01

    A vector-borne disease is one in which the pathogenic microorganism is transmitted from an infected individual to another individual by an arthropod or other agent. The transmission depends upon the attributes and requirements of at least three different Iiving organisms : the pathologic agent which is either a virus, protozoa, bacteria or helminth (worm); the vector, which is commonly an arthropod such as ticks or mosquitoes; and the human host.

  1. Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

    Science.gov (United States)

    Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J

    2018-01-01

    Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

  2. Price and Income Elasticity of Australian Retail Finance: An Autoregressive Distributed Lag (ARDL Approach

    Directory of Open Access Journals (Sweden)

    Helen Higgs

    2014-03-01

    Full Text Available This paper models the price and income elasticity of retail finance in Australia using aggregate quarterly data and an autoregressive distributed lag (ARDL approach. We particularly focus on the impact of the global financial crisis (GFC from 2007 onwards on retail finance demand and analyse four submarkets (period analysed in brackets: owneroccupied housing loans (Sep 1985–June 2010, term loans (for motor vehicles, household goods and debt consolidation, etc. (Dec 1988–Jun 2010, credit card loans (Mar 1990–Jun 2010, and margin loans (Sep 2000–Jun 2010. Other than the indicator lending rates and annual full-time earnings respectively used as proxies for the price and income effects, we specify a large number of other variables as demand factors, particularly reflecting the value of the asset for which retail finance demand is derived. These variously include the yield on indexed bonds as a proxy for inflation expectations, median housing prices, consumer sentiment indices as measures of consumer confidence, motor vehicle and retail trade sales, housing debt-to-housing assets as a measure of leverage, the proportion of protected margin lending, the available credit limit on credit cards, and the All Ordinaries Index. In the long run, we find significant price elasticities only for term loans and margin loans, and significant income elasticities of demand for housing loans, term loans and margin loans. We also find that the GFC only significantly affected the longrun demand for term loans and margin loans. In the short run, we find that the GFC has had a significant effect on the price elasticity of demand for term loans and margin loans. Expected inflation is also a key factor affecting retail finance demand. Overall, most of the submarkets in the analysis indicate that retail finance demand is certainly price inelastic but more income elastic than conventionally thought.

  3. Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.

    Science.gov (United States)

    Langdon, Ruby; Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey

    2017-11-02

    For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH 2 O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH 2 O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.

  4. Vector financial rogue waves

    International Nuclear Information System (INIS)

    Yan, Zhenya

    2011-01-01

    The coupled nonlinear volatility and option pricing model presented recently by Ivancevic is investigated, which generates a leverage effect, i.e., stock volatility is (negatively) correlated to stock returns, and can be regarded as a coupled nonlinear wave alternative of the Black–Scholes option pricing model. In this Letter, we analytically propose vector financial rogue waves of the coupled nonlinear volatility and option pricing model without an embedded w-learning. Moreover, we exhibit their dynamical behaviors for chosen different parameters. The vector financial rogue wave (rogon) solutions may be used to describe the possible physical mechanisms for the rogue wave phenomena and to further excite the possibility of relative researches and potential applications of vector rogue waves in the financial markets and other related fields. -- Highlights: ► We investigate the coupled nonlinear volatility and option pricing model. ► We analytically present vector financial rogue waves. ► The vector financial rogue waves may be used to describe the extreme events in financial markets. ► This results may excite the relative researches and potential applications of vector rogue waves.

  5. Improved inference on cointegrating vectors in the presence of a near unit root using adjusted quantiles

    DEFF Research Database (Denmark)

    Franchi, Massimo; Johansen, Søren

    2017-01-01

    It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper...... formulates a CVAR model allowing for many near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then a critical value adjustment suggested by McCloskey for the test on the cointegrating relations is implemented, and it is found by simulation that it eliminates...... size distortions and has reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs....

  6. Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles

    DEFF Research Database (Denmark)

    Franchi, Massimo; Johansen, Søren

    2017-01-01

    It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper...... formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near...... unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs....

  7. Improved inference on cointegrating vectors in the presence of a near unit root using adjusted quantiles

    DEFF Research Database (Denmark)

    Franchi, Massimo; Johansen, Søren

    It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper...... formulates a CVAR model allowing for many near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then a critical value adjustment suggested by McCloskey for the test on the cointegrating relations is implemented, and it is found by simulation that it eliminates...... size distortions and has reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs....

  8. Application of Bred Vectors To Data Assimilation

    Science.gov (United States)

    Corazza, M.; Kalnay, E.; Patil, Dj

    subspace. The presence of low-dimensional regions in the perturbations of the basic flow has important implications for data assimilation. At any given time, there is a difference between the true atmospheric state and the model forecast. Assuming that model er- rors are not the dominant source of errors, in a region of low BV-dimensionality the difference between the true state and the forecast should lie substantially in the low dimensional unstable subspace of the few bred vectors that contribute most strongly to the low BV-dimension. This information should yield a substantial improvement in the forecast: the data assimilation algorithm should correct the model state by moving it closer to the observations along the unstable subspace, since this is where the true state most likely lies. Preliminary experiments have been conducted with the quasi-geostrophic data assim- ilation system testing whether it is possible to add "errors of the day" based on bred vectors to the standard (constant) 3D-Var background error covariance in order to capture these important errors. The results are extremely encouraging, indicating a significant reduction (about 40%) in the analysis errors at a very low computational cost. References: 2 Corazza, M., E. Kalnay, DJ Patil, R. Morss, M Cai, I. Szunyogh, BR Hunt, E Ott and JA Yorke, 2001: Use of the breeding technique to estimate the structure of the analysis "errors of the day". Submitted to Nonlinear Processes in Geophysics. Hamill, T.M., Snyder, C., and Morss, R.E., 2000: A Comparison of Probabilistic Fore- casts from Bred, Singular-Vector and Perturbed Observation Ensembles, Mon. Wea. Rev., 128, 1835­1851. Kalnay, E., and Z. Toth, 1994: Removing growing errors in the analysis cycle. Preprints of the Tenth Conference on Numerical Weather Prediction, Amer. Meteor. Soc., 1994, 212-215. Morss, R. E., 1999: Adaptive observations: Idealized sampling strategies for improv- ing numerical weather prediction. PHD thesis, Massachussetts Institute

  9. Ochratoxin A production by strains of Aspergillus niger var. niger.

    Science.gov (United States)

    Abarca, M L; Bragulat, M R; Castellá, G; Cabañes, F J

    1994-01-01

    In a survey of the occurrence of ochratoxin A (OA)-positive strains isolated from feedstuffs, two of the 19 isolates of Aspergillus niger var. niger that were studied produced OA in 2% yeast extract-15% sucrose broth and in corn cultures. This is the first report of production of OA by this species. PMID:8074536

  10. Micropropagation of Caralluma stalagmifera var. longipetala : A rare ...

    African Journals Online (AJOL)

    An efficient in vitro protocol has been developed for the multiplication of shoots and conservation of a rare succulent medicinal plant Caralluma stalagmifera var. longipetala growing wildly in Karnataka State. Proliferation of multiple shoots was achieved on Murashige and Skoog's (MS) medium supplemented with various ...

  11. Chemical composition of essential oil of Psidium cattleianum var ...

    African Journals Online (AJOL)

    The aim of this study was to investigate the essential oil composition of Psidium cattleianum var. lucidum from South Africa. The essential oils were extracted by hydrodistillation and the components were identified by gas chromatography coupled to mass spectrometry (GC-MS) to determine the chemical composition of the ...

  12. Testing for Granger causality in large mixed-frequency VARs

    NARCIS (Netherlands)

    Götz, T.B.; Hecq, A.W.

    2014-01-01

    In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels (2012), where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a

  13. Impacts of Bacillus thuringiensis var. israelensis and Bacillus ...

    African Journals Online (AJOL)

    The study assessed the impact of bio-larvicides- Bacillus thuringiensis var. israelensis (Bti) and B. sphaericus (Bs) on anopheline mosquito larval densities in four selected areas of Lusaka urban district. Larval densities were determined using a standard WHO protocol at each study area prior to and after larviciding.

  14. The identity of zostera marina var. angustifolia Hornemann (Potamogetonaceae)

    NARCIS (Netherlands)

    Hartog, den C.

    1972-01-01

    Since Hornemann (Fl. Dan. 9, 1816, p. 3, pl. 1501) published the name Zostera marina var. angustifolia together with a very poor drawing and the extremely short diagnosis ‘foliis subenerviis’ several interpretations of the identity of this taxon have been given. Some authors regarded it as a

  15. A Bootstrap Cointegration Rank Test for Panels of VAR Models

    DEFF Research Database (Denmark)

    Callot, Laurent

    functions of the individual Cointegrated VARs (CVAR) models. A bootstrap based procedure is used to compute empirical distributions of the trace test statistics for these individual models. From these empirical distributions two panel trace test statistics are constructed. The satisfying small sample...

  16. Local cabbage ( Brassica oleracea var. capitata L.) populations from ...

    African Journals Online (AJOL)

    In previous experiments, we were able to augment cabbages (Brassica oleracea L. var. capitata L.) with two new local open pollinated (OP) populations and one cultivar. The type of use indicated that these are cabbages with thinner and juicier leaves, which predisposes their heads for fine grating and also makes their ...

  17. Fiscal developments and financial stress: a threshold VAR analysis

    Czech Academy of Sciences Publication Activity Database

    Afonso, A.; Baxa, Jaromír; Slavík, M.

    2011-01-01

    Roč. 2011, č. 1319 (2011), s. 1-60 ISSN 1725-2806 Institutional research plan: CEZ:AV0Z10750506 Keywords : fiscal policy * financial markets * threshold VAR Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/baxa-0364091.pdf

  18. Anogeissus sericea var. nummalaria King ex Duthie (Combretaceae ...

    Indian Academy of Sciences (India)

    Anogeissus sericea var. nummalaria King ex Duthie (Combretaceae) is moderate sized multipurpose hard wood tree of dry deciduous forests with drooping branches and yellow to brownish-yellow flowers. it is endemic to Rajasthan and is considered to be a threatened tree of the region due to over exploitation for timber ...

  19. Fast Responding Voltage Regulator and Dynamic VAR Compensator

    Energy Technology Data Exchange (ETDEWEB)

    Divan, Deepak [Varentec, Incorporated, San Jose, CA (United States); Moghe, Rohit [Varentec, Incorporated, San Jose, CA (United States); Tholomier, Damien [Varentec, Incorporated, San Jose, CA (United States)

    2014-12-31

    The objectives of this project were to develop a dynamic VAR compensator (DVC) for voltage regulation through VAR support to demonstrate the ability to achieve greater levels of voltage control on electricity distribution networks, and faster response compared to existing grid technology. The goal of the project was to develop a prototype Fast Dynamic VAR Compensator (Fast DVC) hardware device, and this was achieved. In addition to developing the dynamic VAR compensator device, Varentec in partnership with researchers at North Carolina State University (NCSU) successfully met the objectives to model the potential positive impact of such DVCs on representative power networks. This modeling activity validated the ability of distributed dynamic VAR compensators to provide fast voltage regulation and reactive power control required to respond to grid disturbances under high penetration of fluctuating and intermittent distributed energy resources (DERs) through extensive simulation studies. Specifically the following tasks were set to be accomplished: 1) Development of dynamic VAR compensator to support dynamic voltage variations on the grid through VAR control 2) Extensive testing of the DVC in the lab environment 3) Present the operational DVC device to the DOE at Varentec’s lab 4) Formulation of a detailed specification sheet, unit assembly document, test setup document, unit bring-up plan, and test plan 5) Extensive simulations of the DVC in a system with high PV penetration. Understanding the operation with many DVC on a single distribution system 6) Creation and submittal of quarterly and final reports conveying the design documents, unit performance data, modeling simulation charts and diagrams, and summary explanations of the satisfaction of program goals. This report details the various efforts that led to the development of the Fast DVC as well as the modeling & simulation results. The report begins with the introduction in Section II which outlines the

  20. Chemopreventive and Anticancer Activities of Allium victorialis var. platyphyllum Extracts.

    Science.gov (United States)

    Kim, Hyun-Jeong; Park, Min Jeong; Park, Hee-Juhn; Chung, Won-Yoon; Kim, Ki-Rim; Park, Kwang-Kyun

    2014-09-01

    Allium victorialis var. platyphyllum is an edible perennial herb and has been used as a vegetable or as a Korean traditional medicine. Allium species have received much attention owing to their diverse pharmacological properties, including antioxidative, anti-inflammatory, and anticancer activities. However, A. victorialis var. platyphyllum needs more study. The chemopreventive potential of A. victorialis var. platyphyllum methanol extracts was examined by measuring 12-O-tetra-decanoylphorbol 13-acetate (TPA)-induced superoxide anion production in the differentiated HL-60 cells, TPA-induced mouse ear edema, and Ames/Salmonella mutagenicity. The apoptosis-inducing capabilities of the extracts were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, 4',6-diamidino-2-phenylindole staining, and the DNA fragmentation assay in human colon cancer HT-29 cells. Antimetastatic activities of the extracts were also investigated in an experimental mouse lung metastasis model. The methanol extracts of A. victorialis var. platyphyllum rhizome (AVP-R) and A. victorialis var. platyphyllum stem (AVP-S) dose-dependently inhibited the TPA-induced generation of superoxide anion in HL-60 cells and TPA-induced ear edema in mice, as well as 7,12-dimethylbenz[a]anthracene (DMBA) and tert-butyl hydroperoxide (t-BOOH) -induced bacterial mutagenesis. AVP-R and AVP-S reduced cell viability in a dose-related manner and induced apoptotic morphological changes and internucleosomal DNA fragmentation in HT-29 cells. In the experimental mouse lung metastasis model, the formation of tumor nodules in lung tissue was significantly inhibited by the treatment of the extracts. AVP-R and AVP-S possess antioxidative, anti-inflammatory, antimutagenic, proapoptotic, and antimetastatic activities. Therefore, these extracts can serve as a beneficial supplement for the prevention and treatment of cancer.

  1. Amanitin and phallotoxin concentration in Amanita phalloides var. alba mushroom.

    Science.gov (United States)

    Kaya, Ertugrul; Yilmaz, Ismail; Sinirlioglu, Zeynep Aydin; Karahan, Selim; Bayram, Recep; Yaykasli, Kursat Oguz; Colakoglu, Serdar; Saritas, Ayhan; Severoglu, Zeki

    2013-12-15

    Although rarely seen, Amanita phalloides var. alba, a variety of A. phalloides type mushrooms, causes mushroom poisoning resulting in death. Since it is frequently confused with some edible mushrooms due to its white colored cap and macroscopic appearance, it becomes important in toxicological terms. Knowledge of the toxin amount contained in this mushroom type is invaluable in the treatment of cases involving poisoning. In this study, we examined the toxin levels of various parts of the A. phalloides var. alba mushroom growing Duzce region of Turkey. Toxin analyses were carried out for A. phalloides var. alba, which were collected from the forests Duzce region of Turkey in 2011, as a whole and also separately in its spore, pileus, gills, stipe and volva parts. The alpha amanitin, beta amanitin, gamma amanitin, phalloidin and phallacidine analyses of the mushrooms were carried out using the RP-HPLC method. A genetic analysis of the mushroom showed that it had similar genetic characteristics as A. phalloides and was a variety of it. The lowest toxins quantity was detected in spores, volva and stipe among all parts of the mushroom. The maximum amount of amatoxins was measured in the gills. The pileus also contained a high amount of amatoxins. Generally, amatoxins and phallotoxin concentrations were lower as compared to A. phalloides, but interestingly all toxins other than gamma toxin were higher in the spores of A. phalloides var. alba. The amount of toxin in all of its parts had sufficient concentrations to cause death. With this study, the amatoxin and phallotoxin concentrations in A. phalloides var. alba mushroom and in its parts have been revealed in detail for the first time. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. A comprehensive market-based scheme for VAR management and pricing in the electricity markets

    Energy Technology Data Exchange (ETDEWEB)

    El-Araby, E.E. [Qassim Univ., Alqassim, Meldia (Saudi Arabia). Dept. of Electrical Engineering

    2009-07-01

    In order to enable a power system to operate within an acceptable degree of reliability and security, the provision of VAR ancillary services from the VAR sources in electricity markets is the most effective method. The procurement of VAR services is particularly problematic for transmission operators as it relates to pricing mechanism and various technical issues during system operation. This paper proposed an integrated market-based approach for pricing VAR service in the electricity market. The paper was an extension of the authors' proposal for the provision of the VAR service from dynamic VAR sources in a competitive market-based environment. The formulation was modified to include VAR utilization payment and possible power system transition states multiple base cases and contingencies with their associated occurrence probabilities. The paper discussed the basic terms of the proposed approach including the VAR market objective and generator VAR output and its compensation. The mathematical formulation that considered VAR capacity payment, utilization payment and operating costs under the previous transition states in a unified single problem were introduced. The overall problem formulation and solution algorithm were also presented as a large-scale mixed integer nonlinear optimization problem. It was concluded that the proposed method was suited for the simulation and analysis of the existing VAR market. 8 refs., 3 tabs., 5 figs., 2 appendices.

  3. EFEITO FUNGITÓXICO DO ÓLEO DE NIM SOBRE Metarhizium anisopliae var. acridum e Metarhizium anisopliae var. anisopliae

    Directory of Open Access Journals (Sweden)

    Álison Bruno da Silva Santos

    2009-01-01

    Full Text Available Plague control is based almost exclusively on application of chemical substances, however these products are toxic to men and animals and cause odd effects on environment quality. In Plague Integrated Management (PIM, the use of selected insecticides and entomopathogenic fungi should be considered as one viable strategy for plague control in agriculture. This work aimed to evaluate, in laboratory, the compatibility of the entomopathogenic fungi Metarhizium anisopliae var. acridum and Metarhizium anisopliae var. anisopliae with the oil of Nim. The addition of the product was made to the potato-dextrose-agar medium still liquid (±45°C, in a way that the final concentration obeyed 50% of the producer's recommendation. After fungi inoculation, the dishes were incubated in a cimatized room at 28°C, photophase of 12 hours and relative humidity of 75±5% for 12 day period. The number of conidia per colonie was counted with a Neubauer chamber. Statistic delineament was entirely in random, with two treatments (PDA with insecticide, and a control group (PDA without insecticide, and 9 repetitions for each treatment. The results showed that the insecticide inhibited conidial production in Metarhizium anisopliae var. anisopliae strains when compared to the control group. The diameter of Metarhizium anisopliae var. acridum colonies suffered significative reduction in its size, compared to control. The tested insecticide, in the concentration and formulation used, presented compatibility with the tested strains.

  4. Metabolomic variation of brassica rapa var. rapa (var. raapstelen) and raphanus sativus l. at different developmental stages

    NARCIS (Netherlands)

    Jahangir, M.; Abdel-Farid, I.B.; Vos, de C.H.R.; Jonker, H.H.; Choi, Y.H.; Verpoorte, R.

    2014-01-01

    Brassica rapa (var. raapstelen) and Raphanus sativus (red radish) are being used as food and fodder while also known as model in recent plant research due to the diversity of metabolites as well as genetic resemblance to Arabidopsis. This study explains the change in metabolites (amino acids,

  5. Vectorization in quantum chemistry

    International Nuclear Information System (INIS)

    Saunders, V.R.

    1987-01-01

    It is argued that the optimal vectorization algorithm for many steps (and sub-steps) in a typical ab initio calculation of molecular electronic structure is quite strongly dependent on the target vector machine. Details such as the availability (or lack) of a given vector construct in the hardware, vector startup times and asymptotic rates must all be considered when selecting the optimal algorithm. Illustrations are drawn from: gaussian integral evaluation, fock matrix construction, 4-index transformation of molecular integrals, direct-CI methods, the matrix multiply operation. A cross comparison of practical implementations on the CDC Cyber 205, the Cray-IS and Cray-XMP machines is presented. To achieve portability while remaining optimal on a wide range of machines it is necessary to code all available algorithms in a machine independent manner, and to select the appropriate algorithm using a procedure which is based on machine dependent parameters. Most such parameters concern the timing of certain vector loop kernals, which can usually be derived from a 'bench-marking' routine executed prior to the calculation proper

  6. Can reported VaR be used as an indicator of the volatility of share prices? Evidence from UK banks.

    OpenAIRE

    Ou, Shian Kao

    2006-01-01

    Value at Risk (VaR) is used as an indicator to measure the risks contained in a firm. With the uprising development of VaR theory and computational techniques, the VaR is nowadays adopted by banks and reported in annual reports. Since the method to calculate VaR is questioned, and the reported VaR can not be thoroughly audited, this paper attempts to find the relationship between the reported VaR and the volatility of share price for UK listed banks. This paper reviews literature about VaR an...

  7. Vector Fields on Product Manifolds

    OpenAIRE

    Kurz, Stefan

    2011-01-01

    This short report establishes some basic properties of smooth vector fields on product manifolds. The main results are: (i) On a product manifold there always exists a direct sum decomposition into horizontal and vertical vector fields. (ii) Horizontal and vertical vector fields are naturally isomorphic to smooth families of vector fields defined on the factors. Vector fields are regarded as derivations of the algebra of smooth functions.

  8. Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR models

    Directory of Open Access Journals (Sweden)

    Ryan Louise

    2007-11-01

    Full Text Available Abstract Background The Conditional Autoregressive (CAR model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW, Australia within 198 Statistical Local Areas. Between the years 1995–2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995–2000 and their fit evaluated within the following time period: 2001–2003. Results We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age. In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model' was able to correctly classify 35/47 high-risk areas (sensitivity 74% with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori

  9. Real time damage detection using recursive principal components and time varying auto-regressive modeling

    Science.gov (United States)

    Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.

    2018-02-01

    In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving

  10. Application of autoregressive methods and Lyapunov coefficients for instability studies of nuclear reactors

    International Nuclear Information System (INIS)

    Aruquipa Coloma, Wilmer

    2017-01-01

    Nuclear reactors are susceptible to instability, causing oscillations in reactor power in specific working regions characterized by determined values of power and coolant mass flow. During reactor startup, there is a greater probability that these regions of instability will be present; another reason may be due to transient processes in some reactor parameters. The analysis of the temporal evolution of the power reveals a stable or unstable process after the disturbance in a light water reactor of type BWR (Boiling Water Reactor). In this work, the instability problem was approached in two ways. The first form is based on the ARMA (Autoregressive Moving Average models) model. This model was used to calculate the Decay Ratio (DR) and natural frequency (NF) of the oscillations, parameters that indicate if the one power signal is stable or not. In this sense, the DRARMA code was developed. In the second form, the problems of instability were analyzed using the classical concepts of non-linear systems, such as Lyapunov exponents, phase space and attractors. The Lyapunov exponents quantify the exponential divergence of the trajectories initially close to the phase space and estimate the amount of chaos in a system; the phase space and the attractors describe the dynamic behavior of the system. The main aim of the instability phenomena studies in nuclear reactors is to try to identify points or regions of operation that can lead to power oscillations conditions. The two approaches were applied to two sets of signals. The first set comes from signals of instability events of the commercial Forsmark reactors 1 and 2 and were used to validate the DRARMA code. The second set was obtained from the simulation of transient events of the Peach Bottom reactor; for the simulation, the PARCS and RELAP5 codes were used for the neutronic/thermal hydraulic coupling calculation. For all analyzes made in this work, the Matlab software was used due to its ease of programming and

  11. Foco de leishmaniasis en El Hobo, municipio de El Carmen de Bolívar, Bolívar, Colombia

    Directory of Open Access Journals (Sweden)

    Luis Alberto Cortés

    2006-10-01

    Full Text Available Introducción. Se describen las características epidemiológica e importancia de la especies de Lutzomyia presentes en un foco de leishmaniasis en la vereda El Hobo Carmen de Bolívar, departamento de Bolívar. Objetivos. Obtener un conocimiento preliminar de la transmisión de leishmaniasis en la vereda El Hobo Carmen de Bolívar. Materiales y métodos. Se analizaron datos epidemiológicos y se realizaron capturas de flebótomos con trampas CDC y cebo humano en la vereda El Hobo. Para establecer la sero prevalencia de leishmaniasis visceral canina se hizo un estudio en perros mediante la técnica de inmunofluorescencia indirecta. Resultados. Se capturaron nueve especies de Lutzomyia: L. trinidadensis, L. evansi, L .cayennensis, L. venezuelensis, L. gomezi,L. dubitans, L. ylephiletor, L. yuilli, y L. walkeri. Las especie de mayor importancia por sus implicaciones en la transmisión de leishmaniasis cutánea y visceral fueron L. gomezi, y L. evansi respectivamente. Se reporta por primera vez para Bolívar especimenes de L. venezuelensis, L. dubitans, L. ylephiletor, L. yuilli, y L. walkeri. Se determinó una prevalencia de leishmaniasis visceral del 36% en los caninos estudiados. Según los reportes epidemiológicos, en el 2002 en el municipio de Carmen de Bolívar la leishmaniasis cutánea mostró un aumento del 40% y la leishmaniasis visceral canina del 80% de los casos con respecto al 2001, debido al brote presentado en la vereda El Hobo Conclusiones. Los resultados determinan a la vereda El Hobo como una zona de riesgo potencial de transmisión de leishmaniasis cutánea y visceral.

  12. Least squares autoregressive (maximum entropy) spectral estimation for Fourier spectroscopy and its application to the electron cyclotron emission from plasma

    International Nuclear Information System (INIS)

    Iwama, N.; Inoue, A.; Tsukishima, T.; Sato, M.; Kawahata, K.

    1981-07-01

    A new procedure for the maximum entropy spectral estimation is studied for the purpose of data processing in Fourier transform spectroscopy. The autoregressive model fitting is examined under a least squares criterion based on the Yule-Walker equations. An AIC-like criterion is suggested for selecting the model order. The principal advantage of the new procedure lies in the enhanced frequency resolution particularly for small values of the maximum optical path-difference of the interferogram. The usefulness of the procedure is ascertained by some numerical simulations and further by experiments with respect to a highly coherent submillimeter wave and the electron cyclotron emission from a stellarator plasma. (author)

  13. Efficient Blind System Identification of Non-Gaussian Auto-Regressive Models with HMM Modeling of the Excitation

    DEFF Research Database (Denmark)

    Li, Chunjian; Andersen, Søren Vang

    2007-01-01

    We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....

  14. The Shifting Seasonal Mean Autoregressive Model and Seasonality in the Central England Monthly Temperature Series, 1772-2016

    DEFF Research Database (Denmark)

    He, Changli; Kang, Jian; Terasvirta, Timo

    In this paper we introduce an autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed, the decomposition being similar ...... temperature series. More specifically, the idea is to find out in which way and by how much the monthly temperatures are varying over time during the period of more than 240 years, if they do. Misspecification tests are applied to the estimated model and the findings discussed....

  15. Assessment of surface runoff depth changes in S\\varǎţel River basin, Romania using GIS techniques

    Science.gov (United States)

    Romulus, Costache; Iulia, Fontanine; Ema, Corodescu

    2014-09-01

    S\\varǎţel River basin, which is located in Curvature Subcarpahian area, has been facing an obvious increase in frequency of hydrological risk phenomena, associated with torrential events, during the last years. This trend is highly related to the increase in frequency of the extreme climatic phenomena and to the land use changes. The present study is aimed to highlight the spatial and quantitative changes occurred in surface runoff depth in S\\varǎţel catchment, between 1990-2006. This purpose was reached by estimating the surface runoff depth assignable to the average annual rainfall, by means of SCS-CN method, which was integrated into the GIS environment through the ArcCN-Runoff extension, for ArcGIS 10.1. In order to compute the surface runoff depth, by CN method, the land cover and the hydrological soil classes were introduced as vector (polygon data), while the curve number and the average annual rainfall were introduced as tables. After spatially modeling the surface runoff depth for the two years, the 1990 raster dataset was subtracted from the 2006 raster dataset, in order to highlight the changes in surface runoff depth.

  16. Bunyavirus-Vector Interactions

    Directory of Open Access Journals (Sweden)

    Kate McElroy Horne

    2014-11-01

    Full Text Available The Bunyaviridae family is comprised of more than 350 viruses, of which many within the Hantavirus, Orthobunyavirus, Nairovirus, Tospovirus, and Phlebovirus genera are significant human or agricultural pathogens. The viruses within the Orthobunyavirus, Nairovirus, and Phlebovirus genera are transmitted by hematophagous arthropods, such as mosquitoes, midges, flies, and ticks, and their associated arthropods not only serve as vectors but also as virus reservoirs in many cases. This review presents an overview of several important emerging or re-emerging bunyaviruses and describes what is known about bunyavirus-vector interactions based on epidemiological, ultrastructural, and genetic studies of members of this virus family.

  17. Sums and Gaussian vectors

    CERN Document Server

    Yurinsky, Vadim Vladimirovich

    1995-01-01

    Surveys the methods currently applied to study sums of infinite-dimensional independent random vectors in situations where their distributions resemble Gaussian laws. Covers probabilities of large deviations, Chebyshev-type inequalities for seminorms of sums, a method of constructing Edgeworth-type expansions, estimates of characteristic functions for random vectors obtained by smooth mappings of infinite-dimensional sums to Euclidean spaces. A self-contained exposition of the modern research apparatus around CLT, the book is accessible to new graduate students, and can be a useful reference for researchers and teachers of the subject.

  18. Duality in vector optimization

    CERN Document Server

    Bot, Radu Ioan

    2009-01-01

    This book presents fundamentals and comprehensive results regarding duality for scalar, vector and set-valued optimization problems in a general setting. After a preliminary chapter dedicated to convex analysis and minimality notions of sets with respect to partial orderings induced by convex cones a chapter on scalar conjugate duality follows. Then investigations on vector duality based on scalar conjugacy are made. Weak, strong and converse duality statements are delivered and connections to classical results from the literature are emphasized. One chapter is exclusively consecrated to the s

  19. Multithreading in vector processors

    Science.gov (United States)

    Evangelinos, Constantinos; Kim, Changhoan; Nair, Ravi

    2018-01-16

    In one embodiment, a system includes a processor having a vector processing mode and a multithreading mode. The processor is configured to operate on one thread per cycle in the multithreading mode. The processor includes a program counter register having a plurality of program counters, and the program counter register is vectorized. Each program counter in the program counter register represents a distinct corresponding thread of a plurality of threads. The processor is configured to execute the plurality of threads by activating the plurality of program counters in a round robin cycle.

  20. Matrix vector analysis

    CERN Document Server

    Eisenman, Richard L

    2005-01-01

    This outstanding text and reference applies matrix ideas to vector methods, using physical ideas to illustrate and motivate mathematical concepts but employing a mathematical continuity of development rather than a physical approach. The author, who taught at the U.S. Air Force Academy, dispenses with the artificial barrier between vectors and matrices--and more generally, between pure and applied mathematics.Motivated examples introduce each idea, with interpretations of physical, algebraic, and geometric contexts, in addition to generalizations to theorems that reflect the essential structur

  1. Free topological vector spaces

    OpenAIRE

    Gabriyelyan, Saak S.; Morris, Sidney A.

    2016-01-01

    We define and study the free topological vector space $\\mathbb{V}(X)$ over a Tychonoff space $X$. We prove that $\\mathbb{V}(X)$ is a $k_\\omega$-space if and only if $X$ is a $k_\\omega$-space. If $X$ is infinite, then $\\mathbb{V}(X)$ contains a closed vector subspace which is topologically isomorphic to $\\mathbb{V}(\\mathbb{N})$. It is proved that if $X$ is a $k$-space, then $\\mathbb{V}(X)$ is locally convex if and only if $X$ is discrete and countable. If $X$ is a metrizable space it is shown ...

  2. Scalar-vector bootstrap

    Energy Technology Data Exchange (ETDEWEB)

    Rejon-Barrera, Fernando [Institute for Theoretical Physics, University of Amsterdam,Science Park 904, Postbus 94485, 1090 GL, Amsterdam (Netherlands); Robbins, Daniel [Department of Physics, Texas A& M University,TAMU 4242, College Station, TX 77843 (United States)

    2016-01-22

    We work out all of the details required for implementation of the conformal bootstrap program applied to the four-point function of two scalars and two vectors in an abstract conformal field theory in arbitrary dimension. This includes a review of which tensor structures make appearances, a construction of the projectors onto the required mixed symmetry representations, and a computation of the conformal blocks for all possible operators which can be exchanged. These blocks are presented as differential operators acting upon the previously known scalar conformal blocks. Finally, we set up the bootstrap equations which implement crossing symmetry. Special attention is given to the case of conserved vectors, where several simplifications occur.

  3. The impact of TV mass media campaigns on calls to a National Quitline and the use of prescribed nicotine replacement therapy: a structural vector autoregression analysis.

    Science.gov (United States)

    Haghpanahan, Houra; Mackay, Daniel F; Pell, Jill P; Bell, David; Langley, Tessa; Haw, Sally

    2017-07-01

    To estimate (1) the immediate impact; (2) the cumulative impact; and (3) the duration of impact of Scottish tobacco control TV mass media campaigns (MMCs) on smoking cessation activity, as measured by calls to Smokeline and the volume of prescribed nicotine replacement therapy (NRT). Multivariate time-series analysis using secondary data on population level measures of exposure to TV MMCs broadcast and smoking cessation activity between 2003 and 2012. Population of Scotland. Adult television viewer ratings (TVRs) as a measure of exposure to Scottish mass media campaigns in the adult population; monthly calls to NHS Smokeline; and the monthly volume of prescribed NRT as measured by gross ingredient costs (GIC). Tobacco control TVRs were associated with an increase in calls to Smokeline but not an increase in the volume of prescribed NRT. A 1 standard deviation (SD) increase of 194 tobacco control TVRs led to an immediate and significant increase of 385.9 [95% confidence interval (CI) = 171.0, 600.7] calls to Smokeline (unadjusted model) within 1 month. When adjusted for seasonality the impact was reduced, but the increase in calls remained significant (226.3 calls, 95% CI = 37.3, 415.3). The cumulative impact on Smokeline calls remained significant for 6 months after broadcast in the unadjusted model and 18 months in the adjusted model. However, an increase in tobacco control TVRs of 194 failed to have a significant impact on the GIC of prescribed NRT in either the unadjusted (£1361.4, 95% CI = -£9138.0, £11860.9) or adjusted (£6297.1, 95% CI = -£2587.8, £15182.1) models. Tobacco control television mass media campaigns broadcast in Scotland between 2003 and 2012 were effective in triggering calls to Smokeline, but did not increase significantly the use of prescribed nicotine replacement therapy by adult smokers. The impact on calls to Smokeline occurred immediately within 1 month of broadcast and was sustained for at least 6 months. © 2017 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.

  4. Identification of the relationship between Chinese Adiantum reniforme var. sinense and Canary Adiantum reniforme.

    Science.gov (United States)

    Wang, Ai-Hua; Sun, Ye; Schneider, Harald; Zhai, Jun-Wen; Liu, Dong-Ming; Zhou, Jin-Song; Xing, Fu-Wu; Chen, Hong-Feng; Wang, Fa-Guo

    2015-02-05

    There are different opinions about the relationship of two disjunctively distributed varieties Adiantum reniforme L. var. sinense Y.X.Lin and Adiantum reniforme L. Adiantum reniforme var. sinense is an endangered fern only distributed in a narrowed region of Chongqing city in China, while Adiantum reniforme var. reniforme just distributed in Canary Islands and Madeira off the north-western African coast. To verify the relationship of these two taxa, relative phylogenetic analyses, karyotype analyses, microscopic spore observations and morphological studies were performed in this study. Besides, divergence time between A. reniforme var. sinense and A. reniforme var. reniforme was estimated using GTR model according to a phylogeny tree constructed with the three cpDNA markers atpA, atpB, and rbcL. Phylogenetic results and divergence time analyses--all individuals of A. reniforme var. sinense from 4 different populations (representing all biogeographic distributions) were clustered into one clade and all individuals of A. reniforme var. reniforme from 7 different populations (all biogeographic distributions are included) were clustered into another clade. The divergence between A. reniforme var. reniforme and A. reniforme var. sinense was estimated to be 4.94 (2.26-8.66) Myr. Based on karyotype analyses, A. reniforme var. reniforme was deduced to be hexaploidy with 2n = 180, X = 30, while A. reniforme var. sinense was known as tetraploidy. Microscopic spore observations suggested that surface ornamentation of A. reniforme var. reniforme is psilate, but that of A. reniforme var. sinense is rugate. Leaf blades of A. reniforme var. sinense are membranous and reniform and with several obvious concentric rings, and leaves of A. reniforme var. reniforme are pachyphyllous and coriaceous and are much rounder and similar to palm. Adiantum reniforme var. sinense is an independent species rather than the variety of Adiantum reniforme var. reniforme. As a result, we

  5. Estimation of vector velocity

    DEFF Research Database (Denmark)

    2000-01-01

    Using a pulsed ultrasound field, the two-dimensional velocity vector can be determined with the invention. The method uses a transversally modulated ultrasound field for probing the moving medium under investigation. A modified autocorrelation approach is used in the velocity estimation. The new...

  6. Production of lentiviral vectors

    Directory of Open Access Journals (Sweden)

    Otto-Wilhelm Merten

    2016-01-01

    Full Text Available Lentiviral vectors (LV have seen considerably increase in use as gene therapy vectors for the treatment of acquired and inherited diseases. This review presents the state of the art of the production of these vectors with particular emphasis on their large-scale production for clinical purposes. In contrast to oncoretroviral vectors, which are produced using stable producer cell lines, clinical-grade LV are in most of the cases produced by transient transfection of 293 or 293T cells grown in cell factories. However, more recent developments, also, tend to use hollow fiber reactor, suspension culture processes, and the implementation of stable producer cell lines. As is customary for the biotech industry, rather sophisticated downstream processing protocols have been established to remove any undesirable process-derived contaminant, such as plasmid or host cell DNA or host cell proteins. This review compares published large-scale production and purification processes of LV and presents their process performances. Furthermore, developments in the domain of stable cell lines and their way to the use of production vehicles of clinical material will be presented.

  7. Orthogonalisation of Vectors

    Indian Academy of Sciences (India)

    The Gram-Schmidt process is one of the first things one learns in a course ... We might want to stay as close to the experimental data as possible when converting these vectors to orthonormal ones demanded by the model. The process of finding the closest or- thonormal .... is obtained by writing the matrix A = [aI, an], then.

  8. From vectors to mnesors

    OpenAIRE

    Champenois, Gilles

    2007-01-01

    The mnesor theory is the adaptation of vectors to artificial intelligence. The scalar field is replaced by a lattice. Addition becomes idempotent and multiplication is interpreted as a selection operation. We also show that mnesors can be the foundation for a linear calculus.

  9. Calculus with vectors

    CERN Document Server

    Treiman, Jay S

    2014-01-01

    Calculus with Vectors grew out of a strong need for a beginning calculus textbook for undergraduates who intend to pursue careers in STEM. fields. The approach introduces vector-valued functions from the start, emphasizing the connections between one-variable and multi-variable calculus. The text includes early vectors and early transcendentals and includes a rigorous but informal approach to vectors. Examples and focused applications are well presented along with an abundance of motivating exercises. All three-dimensional graphs have rotatable versions included as extra source materials and may be freely downloaded and manipulated with Maple Player; a free Maple Player App is available for the iPad on iTunes. The approaches taken to topics such as the derivation of the derivatives of sine and cosine, the approach to limits, and the use of "tables" of integration have been modified from the standards seen in other textbooks in order to maximize the ease with which students may comprehend the material. Additio...

  10. On vector equilibrium problem

    Indian Academy of Sciences (India)

    [G] Giannessi F, Theorems of alternative, quadratic programs and complementarity problems, in: Variational Inequalities and Complementarity Problems (eds) R W Cottle, F Giannessi and J L Lions (New York: Wiley) (1980) pp. 151±186. [K1] Kazmi K R, Existence of solutions for vector optimization, Appl. Math. Lett. 9 (1996).

  11. Vector-borne Infections

    Centers for Disease Control (CDC) Podcasts

    2011-04-18

    This podcast discusses emerging vector-borne pathogens, their role as prominent contributors to emerging infectious diseases, how they're spread, and the ineffectiveness of mosquito control methods.  Created: 4/18/2011 by National Center for Emerging Zoonotic and Infectious Diseases (NCEZID).   Date Released: 4/27/2011.

  12. [Photosynthetic parameters and physiological indexes of Paris polyphylla var. yunnanensis influenced by arbuscular mycorrhizal fungi].

    Science.gov (United States)

    Wei, Zheng-xin; Guo, Dong-qin; Li, Hai-feng; Ding, Bo; Zhang, Jie; Zhou, Nong; Yu, Jie

    2015-10-01

    Through potted inoculation test at room temperature and indoor analysis, the photosynthetic parameters and physiological and biochemical indexes of Paris polyphylla var. yunnanensis were observed after 28 arbuscular mycorrhizal (AM) fungi were injected into the P. polyphylla var. yunnanensis growing in a sterile soil environment. The results showed that AM fungi established a good symbiosis with P. polyphylla var. yunnanensis. The AM fungi influenced the photosynthetic parameters and physiological and biochemical indexes of P. polyphylla var. yunnanensis. And the influences were varied depending on different AM fungi. The application of AM fungi improved photosynthesis intensity of P. polyphylla var. yunnanensis mesophyll cells, the contents of soluble protein and soluble sugar, protective enzyme activity of P. polyphylla var. yunnanensis leaf, which was beneficial to resist the adverse environment and promote the growth of P. polyphylla var. yunnanensis. Otherwise, there was a certain mutual selectivity between P. polyphylla var. yunnanensis and AM fungi. From the comprehensive effect of inoculation, Racocetra coralloidea, Scutellospora calospora, Claroideoglomus claroideum, S. pellucida and Rhizophagus clarus were the most suitable AM fungi to P. polyphylla var. yunnanensis when P. polyphylla var. yunnanensis was planted in the field.

  13. [Physiological characteristics of Pinus densiflora var. zhangwuensis and Pinus sylvestris var. mongolica seedlings on sandy lands under salt-alkali stresses].

    Science.gov (United States)

    Meng, Peng; Li, Yu-Ling; Zhang, Bai-xi

    2013-02-01

    For the popularization of Pinus densiflora var. zhangwuensis, a new afforestation tree species on the desertified and salinized-alkalized lands in Northern China, and to evaluate the salinity-alkalinity tolerance of the tree species and to better understand the tolerance mechanisms, a pot experiment with 4-year old P. densiflora var. zhangwuensis and P. sylvestris var. mongolica was conducted to study their seedlings growth and physiological and biochemical indices under the effects of three types salt (NaCl, Na2CO3, and NaHCO3 ) stresses and of alkali (NaOH) stress. Under the salt-alkali stresses, the injury level of P. densiflora var. zhangwuensis was lower, and the root tolerance index was higher. The leaf catalase (CAT) activity increased significantly by 22. 6 times at the most, as compared with the control; the leaf malondialdehyde (MDA) content had no significant increase; the leaf chlorophyll (Chl) content had a smaller decrement; and the leaf water content (LWC) increased slightly. P. sylvestris var. mongolica responded differently to the salt-alkali stresses. Its leaf CAT activity had less change, MDA content increased significantly, Chl content had significant decrease, and LWC decreased slightly. It was suggested that P. densi-flora var. zhangwuensis had a greater salinity-alkalinity tolerance than P. sylvestris var. mongolica. The higher iron concentration in P. densiflora var. zhangwuensis needles enhanced the CAT activity and Chl content, whereas the higher concentrations of zinc and copper were associated with the stronger salinity-alkalinity tolerance.

  14. The complete chloroplast genome sequence of Dianthus superbus var. longicalycinus.

    Science.gov (United States)

    Gurusamy, Raman; Lee, Do-Hyung; Park, SeonJoo

    2016-05-01

    The complete chloroplast genome (cpDNA) sequence of Dianthus superbus var. longicalycinus is an economically important traditional Chinese medicine was reported and characterized. The cpDNA of Dianthus superbus var. longicalycinus is 149,539 bp, with 36.3% GC content. A pair of inverted repeats (IRs) of 24,803 bp is separated by a large single-copy region (LSC, 82,805 bp) and a small single-copy region (SSC, 17,128 bp). It encodes 85 protein-coding genes, 36 tRNA genes and 8 rRNA genes. Of 129 individual genes, 13 genes encoded one intron and three genes have two introns.

  15. [Study on quality standard of Mucuna pruriens var. utilis].

    Science.gov (United States)

    Wu, Shi-Hong; Jiang, Wei-Zhe; Lv, Li; Wu, Ling-Ling; Lv, Cong; Shi, Xiao-Xia; Su, Gui-Liang

    2009-03-01

    To provide scientific basis for the utilization and development of Mucuna pruriens var. utilis by establishing its quality control standard. The bioactive constituents were analyzed by TLC and HPLC. Moisture, ash and the extracts of Mucuna pruriens var. utilis were all determined. The TLC spots of levodopa had similar color with the control group at the same position. The results of HPLC quantitative analysis showed that the linear range of levodopa was 26.45 to approximately 132.25 microg/mL, r = 0.9992, and the average recovery rate was 103.8%, RSD = 1.85%. This method is convenient, accurate, reliable with good reproducibility, so it can be used to establish quality standard for the medicinal material.

  16. BRINE SHRIMP LETHALITY BIOASSAY OF GLAUCIUM GRANDIFLORUM VAR. GRANDIFLORUM

    OpenAIRE

    A. SARI, Ç. ÜNSAL, İ. SARIOĞLU, A. SARI, Ç. ÜNSAL, İ. SARIOĞLU

    2013-01-01

    Türkiye'nin 3 farklı bölgesinden toplanan Glaucium grandiflorum Boiss. et Huet var. grandiflorum örneklerinin toprak üstü kısımlarından elde edilen alkaloit ekstreleri ve bu ekstrelerden elde edilen majör alkaloitler allokriptopin, protopİn, (+)-izokoridin, (+)-korİdin üzerinde brİne shrimp lethality testi yapılarak sitotoksisiteleri İncelenmiştir. Glaucium grandiflorum var. grandiflorum türünün 3 örneği de önemli oranda sitotoksik aktİvite göstermiştir. Allokriptopin, protopin, (+)-izok...

  17. VaR: Exchange Rate Risk and Jump Risk

    Directory of Open Access Journals (Sweden)

    Fen-Ying Chen

    2010-01-01

    Full Text Available Incorporating the Poisson jumps and exchange rate risk, this paper provides an analytical VaR to manage market risk of international portfolios over the subprime mortgage crisis. There are some properties in the model. First, different from past studies in portfolios valued only in one currency, this model considers portfolios not only with jumps but also with exchange rate risk, that is vital for investors in highly integrated global financial markets. Second, in general, the analytical VaR solution is more accurate than historical simulations in terms of backtesting and Christoffersen's independence test (1998 for small portfolios and large portfolios. In other words, the proposed model is reliable not only for a portfolio on specific stocks but also for a large portfolio. Third, the model can be regarded as the extension of that of Kupiec (1999 and Chen and Liao (2009.

  18. MANAGEMENT OF Amburana cearensis var. acreana IN ACRE STATE, BRAZIL

    Directory of Open Access Journals (Sweden)

    Evaldo Muñoz Braz

    2014-06-01

    Full Text Available http://dx.doi.org/10.5902/1980509814586This work has as its objectives: a to assess the geographical distribution and population structure of Amburana cearensis var. acreana; b to calculate sustainable cutting rates, according to stipulated cutting cycles, and c to simulate the projected recovery potential in volume based on the calculated cutting rate. It was used data from sustainable forest management plans, and the results will contribute for future decisions about its endangered condition. The results did not corroborate the information that Amburana cearensis var. acreana is endangered in Acre state. However the management sustainability will only be feasible if considered the ideal remaining population structure and the estimative of the optimal cutting rate according to the cutting cycle.

  19. Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model

    Science.gov (United States)

    Vazifedan, Turaj; Shitan, Mahendran

    Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

  20. Autoregressive models as a tool to discriminate chaos from randomness in geoelectrical time series: an application to earthquake prediction

    Directory of Open Access Journals (Sweden)

    C. Serio

    1997-06-01

    Full Text Available The time dynamics of geoelectrical precursory time series has been investigated and a method to discriminate chaotic behaviour in geoelectrical precursory time series is proposed. It allows us to detect low-dimensional chaos when the only information about the time series comes from the time series themselves. The short-term predictability of these time series is evaluated using two possible forecasting approaches: global autoregressive approximation and local autoregressive approximation. The first views the data as a realization of a linear stochastic process, whereas the second considers the data points as a realization of a deterministic process, supposedly non-linear. The comparison of the predictive skill of the two techniques is a test to discriminate between low-dimensional chaos and random dynamics. The analyzed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy in one of the most seismic areas of the Mediterranean region. Our findings are that the global (linear approach is superior to the local one and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. Power spectra of the filtered time series follow a P(f = F-a scaling law: they exhibit the typical behaviour of a broad class of fractal stochastic processes and they are a signature of the self-organized systems.

  1. (Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

    Science.gov (United States)

    Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M

    2017-01-01

    The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

  2. MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

    Directory of Open Access Journals (Sweden)

    MEHDI AMIAN

    2013-10-01

    Full Text Available Functional near infrared spectroscopy (fNIRS is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2 and deoxyhemoglobin (HHb concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR is about 2 dB higher for ARMA based method.

  3. [Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

    Science.gov (United States)

    Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

    2018-04-01

    Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

  4. The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models

    Directory of Open Access Journals (Sweden)

    Mei-Yu LEE

    2014-11-01

    Full Text Available This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0, whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients.

  5. Probiotic Activity of Saccharomyces cerevisiae var. boulardii Against Human Pathogens

    Directory of Open Access Journals (Sweden)

    Katarzyna Rajkowska

    2012-01-01

    Full Text Available Infectious diarrhoea is associated with a modification of the intestinal microflora and colonization of pathogenic bacteria. Tests were performed for seven probiotic yeast strains of Saccharomyces cerevisiae var. boulardii, designated for the prevention and treatment of diarrhoea. To check their possible effectiveness against diarrhoea of different etiologies, the activity against a variety of human pathogenic or opportunistic bacteria was investigated in vitro. In mixed cultures with S. cerevisiae var. boulardii, a statistically significant reduction was observed in the number of cells of Listeria monocytogenes, Pseudomonas aeruginosa and Staphylococcus aureus, by even 55.9 % in the case of L. monocytogenes compared with bacterial monocultures. The influence of yeasts was mostly associated with the shortening of the bacterial lag phase duration, more rapid achievement of the maximum growth rates, and a decrease by 4.4–57.1 % (L. monocytogenes, P. aeruginosa, or an increase by 1.4–70.6 % (Escherichia coli, Enterococcus faecalis, Salmonella Typhimurium in the exponential growth rates. Another issue included in the research was the ability of S. cerevisiae var. boulardii to bind pathogenic bacteria to its cell surface. Yeasts have shown binding capacity of E. coli, S. Typhimurium and additionally of S. aureus, Campylobacter jejuni and E. faecalis. However, no adhesion of L. monocytogenes and P. aeruginosa to the yeast cell wall was noted. The probiotic activity of S. cerevisiae var. boulardii against human pathogens is related to a decrease in the number of viable and active cells of bacteria and the binding capacity of yeasts. These processes may limit bacterial invasiveness and prevent bacterial adherence and translocation in the human intestines.

  6. Phytochemical study of the flavonoids of acacia nilotica var astringens

    International Nuclear Information System (INIS)

    El Gazali, N. A.

    2006-08-01

    The barks of acaica nilotica var astringens were extracted with 95% ethanol. Qualitative tests on the alcoholic extractives were negative for alkaloids and positive for steroids, tannins and flavonoids. Fractionation of the alcoholic extract over silica gel using acetone: methanol (4:1) gave a pure compound-compound 1. The structure of compound 1 was deduced on the basis of its IR, UV, NMR and mass spectra and the following structure was suggested.(Author)

  7. Fiscal developments and financial stress: a threshold VAR analysis

    Czech Academy of Sciences Publication Activity Database

    Afonso, A.; Baxa, Jaromír; Slavík, M.

    2018-01-01

    Roč. 54, č. 2 (2018), s. 395-423 ISSN 0377-7332 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Fiscal policy * Financial markets * Threshold VAR Subject RIV: AH - Economic s OBOR OECD: Applied Economic s, Econometrics Impact factor: 0.645, year: 2016 http://library.utia.cas.cz/separaty/2018/E/baxa-0488303.pdf

  8. A Structural VAR Approach to Estimating Budget Balance Targets

    OpenAIRE

    Robert A Buckle; Kunhong Kim; Julie Tam

    2001-01-01

    The Fiscal Responsibility Act 1994 states that, as a principle of responsible fiscal management, a New Zealand government should ensure total Crown debt is at a prudent level by ensuring total operating expenses do not exceed total operating revenues. In this paper a structural VAR model is estimated to evaluate the impact on the government's cash operating surplus (or budget balance) of four independent disturbances: supply, fiscal, real private demand, and nominal disturbances. Based on the...

  9. REDESCUBRIMIENTO DE MYRRHINIUM ATROPURPUREUM VAR. OCTANDRUM (MYRTACEAE: MYRTINAE EN COLOMBIA

    Directory of Open Access Journals (Sweden)

    PARRA-O. CARLOS

    2003-12-01

    Full Text Available Se documenta el redescubrimiento de Myrrhinium atropurpureum var. octandrum(Myrteae DC., Myrtaceae, taxón prácticamente desconocido en Colombia. Se presentala descripción del taxón, así como notas sobre su distribución en Colombia, elhábitat en que se desarrolla y su posible estatus dentro de las categorías de la ListaRoja de la UICN.

  10. Labor Costs and Foreign Direct Investment: A Panel VAR Approach

    OpenAIRE

    Bahar Bayraktar-Sağlam; Selin Sayek Böke

    2017-01-01

    This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the d...

  11. Exploiting the cointegration properties of U.S. pork - related markets

    DEFF Research Database (Denmark)

    Babula, Ronald; Lund, Mogens

    We apply methods of the cointegrated vector autoregression or VAR model to quar-terly U.S. pork-related markets, from the farm gate upstream, to the downstream markets for processed pork and sausage. This study extends prior VAR econometric work by concentrating on the upstream/downstream relatio......We apply methods of the cointegrated vector autoregression or VAR model to quar-terly U.S. pork-related markets, from the farm gate upstream, to the downstream markets for processed pork and sausage. This study extends prior VAR econometric work by concentrating on the upstream....../downstream relationships between the U.S. farm market for pork and markets for processed pork and sausage. Results include a U.S. long run demand for pork, as well as empirical estimates of specific market events on these three pork-related markets....

  12. CO2 emissions and financial development in an emerging economy: An augmented VAR approach

    International Nuclear Information System (INIS)

    Abbasi, Faiza; Riaz, Khalid

    2016-01-01

    This paper explores the influence of economic and financial development on carbon emissions in a small emerging economy. The study employs ARDL approach to investigate the long run relationship between carbon emissions and a set of economic and financial variables, an Error Correction Model (ECM) to capture the short run dynamics, Granger causality in an augmented VAR framework to check the causality direction, and variance decomposition based on an estimated Vector Error Correction Model (VECM) to determine the relative contributions of economic and financial variables to the evolution of per capita carbon emissions. The periods considered were the full sample (1971–2011), and a reduced sample sub-period (1988–2011) that corresponded to greater liberalization and financial sector development. The financial variables played a role in emission mitigation only in the latter period where greater degree of liberalization and financial sector development occurred. Even then the relative magnitude of emissions mitigation attributable to financial variables was much smaller compared to the emissions raising impact of rising per capita incomes. This underscores the need for adopting other mitigation policies for reducing carbon footprints in those emerging economies where a sufficient degree of financial deepening and financial sector development has not yet taken place. - Highlights: • In economies where structural transformation of financial sector is in early stages. • Financial development does not aid in mitigating CO 2 emissions rather it increases it. • CO 2 emissions rise as per capita income rises. • Government should devise comprehensive and realistic mitigation strategies.

  13. Testing Fiscal Dominance Hypothesis in a Structural VAR Specification for Pakistan

    Directory of Open Access Journals (Sweden)

    Shaheen Rozina

    2018-03-01

    Full Text Available This research aims to test the fiscal dominance hypothesis for Pakistan through a bivariate structural vector auto regression (SVAR specification, covering time period 1977 – 2016. This study employs real primary deficit (non interest government expenditures minus total revenues and real primary liabilities (sum of monetary base and domestic public debt as indicators of fiscal measures and monetary policy respectively. A structural VAR is retrieved both for entire sample period and four sub periods (1977 – 1986, 1987 – 1997, 1998 – 2008, and 2009 – 2016. This study identifies the presence of fiscal dominance for the entire sample period and the sub period from 1987 – 2008. The estimates reveal an interesting phenomenon that fiscal dominance is significant in the elected regimes and weaker in the presence of military regimes in Pakistan. From a policy perspective, this research suggests increased autonomy of central bank to achieve long term price stability and reduced administration costs to ensure efficient democratic regime in Pakistan.

  14. Metabolomic variation of brassica rapa var. rapa (var. raapstelen) and raphanus sativus l. at different developmental stages

    International Nuclear Information System (INIS)

    Jahangir, M.; Farid, I.B.A.

    2014-01-01

    Brassica rapa (var. raapstelen) and Raphanus sativus (red radish) are being used as food and fodder while also known as model in recent plant research due to the diversity of metabolites as well as genetic resemblance to Arabidopsis. This study explains the change in metabolites (amino acids, organic acids, chlorophyll, carotenoids, tocopherols, ascorbic acid, sucrose, phenylpropanoids and glucosinolates) during plant development. In present study the metabolomic variation in relation to plant growth has been evaluated, for Brassica rapa (var. raapstelen) and red radish (Raphanus sativus) at three different developmental stages. A non-targeted and targeted metabolomic approach by NMR and HPLC in combination with Principal component analysis (PCA) of the data was used to identify phytochemicals being influenced by plant growth. The results lead to the better understanding of metabolic changes during plant development and show the importance of plant age with respect to the metabolomic profile of vegetables. (author)

  15. Complete mitochondrial genome of Xingguo red carp (Cyprinus carpio var. singuonensis) and purse red carp (Cyprinus carpio var. wuyuanensis).

    Science.gov (United States)

    Hu, Guang-Fu; Liu, Xiang-Jiang; Li, Zhong; Liang, Hong-Wei; Hu, Shao-Na; Zou, Gui-Wei

    2016-01-01

    The complete mitochondrial genomes of Xingguo red carp (Cyprinus carpio var. singuonensis) and purse red carp (Cyprinus carpio var. wuyuanensis) were sequenced. Comparison of these two mitochondrial genomes revealed that the mtDNAs of these two common carp varieties were remarkably similar in genome length, gene order and content, and AT content. However, size variation between these two mitochondrial genomes presented here showed 39 site differences in overall length. About 2 site differences were located in rRNAs, 3 in tRNAs, 3 in the control region, 31 in protein-coding genes. Thirty-one variable bases in the protein-coding regions between the two varieties mitochondrial sequences led to three variable amino acids, which were mainly located in the protein ND5 and ND4.

  16. Vector grammars and PN machines

    Institute of Scientific and Technical Information of China (English)

    蒋昌俊

    1996-01-01

    The concept of vector grammars under the string semantic is introduced.The dass of vector grammars is given,which is similar to the dass of Chomsky grammars.The regular vector grammar is divided further.The strong and weak relation between the vector grammar and scalar grammar is discussed,so the spectrum system graph of scalar and vector grammars is made.The equivalent relation between the regular vector grammar and Petri nets (also called PN machine) is pointed.The hybrid PN machine is introduced,and its language is proved equivalent to the language of the context-free vector grammar.So the perfect relation structure between vector grammars and PN machines is formed.

  17. VAR, stress-testing and supplementary methodologies: uses and constraints in energy risk management

    International Nuclear Information System (INIS)

    Senior, Brian

    1999-01-01

    This chapter lists some of the special risks associated with a range of energy markets, and questions what is risk. Market risk, the use of value-at-risk (VAR) for measuring and managing market risk, use of VAR in the banking sector, back-testing of VAR, the corporate sector, making investment decisions, and the need for additional methods of risk analysis are discussed. Scenario analysis and stress testing, liquidity, and combining VAR and stress-testing are described. Credit risk and the quantitative analysis of credit risk are addressed, and operational risk, and organisational challenges are considered. Panels present examples of a simple VAR calculation and give descriptions of VAR in corporate decisions, the measurement of liquidity, and the use of the Greeks in decisions on day to day trading and risk management

  18. Mosquito Passage Dramatically Changes var Gene Expression in Controlled Human Plasmodium falciparum Infections.

    Science.gov (United States)

    Bachmann, Anna; Petter, Michaela; Krumkamp, Ralf; Esen, Meral; Held, Jana; Scholz, Judith A M; Li, Tao; Sim, B Kim Lee; Hoffman, Stephen L; Kremsner, Peter G; Mordmüller, Benjamin; Duffy, Michael F; Tannich, Egbert

    2016-04-01

    Virulence of the most deadly malaria parasite Plasmodium falciparum is linked to the variant surface antigen PfEMP1, which is encoded by about 60 var genes per parasite genome. Although the expression of particular variants has been associated with different clinical outcomes, little is known about var gene expression at the onset of infection. By analyzing controlled human malaria infections via quantitative real-time PCR, we show that parasite populations from 18 volunteers expressed virtually identical transcript patterns that were dominated by the subtelomeric var gene group B and, to a lesser extent, group A. Furthermore, major changes in composition and frequency of var gene transcripts were detected between the parental parasite culture that was used to infect mosquitoes and Plasmodia recovered from infected volunteers, suggesting that P. falciparum resets its var gene expression during mosquito passage and starts with the broad expression of a specific subset of var genes when entering the human blood phase.

  19. Twenty novel polymorphic microsatellite primers in the critically endangered Melastoma tetramerum var. tetramerum (Melastomataceae).

    Science.gov (United States)

    Narita, Ayu; Izuno, Ayako; Komaki, Yoshiteru; Tanaka, Takefumi; Murata, Jin; Isagi, Yuji

    2016-09-01

    Microsatellite markers were identified for Melastoma tetramerum var. tetramerum (Melastomataceae), a critically endangered shrub endemic to the Bonin Islands, to reveal genetic characteristics in wild and restored populations. Using next-generation sequencing, 27 microsatellite markers were identified. Twenty of these markers were polymorphic in M. tetramerum var. tetramerum, with two to nine alleles per locus and expected heterozygosity ranging from 0.10 to 0.71. Among the 20 polymorphic markers, 15 were applicable to other closely related taxa, namely M. tetramerum var. pentapetalum, M. candidum var. candidum, and M. candidum var. alessandrense. These markers can be potentially useful to investigate the genetic diversity, population genetic structure, and reproductive ecology of M. tetramerum var. tetramerum as well as of the three related taxa to provide appropriate genetic information for conservation.

  20. Foliar flavonoids from Tanacetum vulgare var. boreale and their geographical variation.

    Science.gov (United States)

    Uehara, Ayumi; Akiyama, Shinobu; Iwashina, Tsukasa

    2015-03-01

    Foliar flavonoids of Tanacetum vulgare var. boreale were isolated. Eight flavonoid glycosides, 7-O-glucosides of apigenin, luteolin, scutellarein and 6- hydroxyluteolin, and 7-O-glucuronides of apigenin, luteolin, chrysoeriol and eriodictyol were identified. Moreover, eight flavonoid aglycones, apigenin, luteolin, hispidulin, nepetin, eupatilin, jaceosidin, pectolinarigenin and axillarin were also isolated and identified. The flavonoid composition of two varieties of T. vulgare, i.e. var. boreale and var. vulgare, were compared. All samples of var. boreale and one sample of var. vulgare had the same flavonoid pattern, and could be distinguished from almost all the samples of var. vulgare. Thus, the occurrence of chemotypes, which are characterized by either the presence or absence of scutellarein 7-O-glucoside, eriodictyol 7-O-glucuronide and pectolinarigenin was shown in T. vulgare sensu lato.