Nonparametric Cointegration Analysis of Fractional Systems With Unknown Integration Orders
DEFF Research Database (Denmark)
Nielsen, Morten Ørregaard
2009-01-01
of a particular model and is invariant to short-run dynamics. Nor does it require the choice of any smoothing parameters that change the test statistic without being reflected in the asymptotic distribution. Furthermore, a consistent estimator of the cointegration space can be obtained from the procedure....... The asymptotic distribution theory for the proposed test is non-standard but easily tabulated. Monte Carlo simulations demonstrate excellent finite sample properties, even rivaling those of well-specified parametric tests. The proposed methodology is applied to the term structure of interest rates, where...
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
A Statistical Analysis of Cointegration for I(2) Variables
DEFF Research Database (Denmark)
Johansen, Søren
1995-01-01
be conducted using the ¿ sup2/sup distribution. It is shown to what extent inference on the cointegration ranks can be conducted using the tables already prepared for the analysis of cointegration of I(1) variables. New tables are needed for the test statistics to control the size of the tests. This paper...... contains a multivariate test for the existence of I(2) variables. This test is illustrated using a data set consisting of U.K. and foreign prices and interest rates as well as the exchange rate....
COINTEGRATION ANALYSIS OF PURCHASING POWER PARITY IN REPUBLIC OF CROATIA
Directory of Open Access Journals (Sweden)
Ivan Kozul
2013-12-01
Full Text Available This paper examines the validity of purchasing power parity (PPP hypothesis in the Republic of Croatia. The main aim is to test whether the PPP Theory holds in the case of the Republic of Croatia and whether the PPP Theory is an appropriate method on which monetary policy makers can rely in determining the size of the market exchange rate deviations from its long-run value. The PPP Theory in the Republic of Croatia has been tested using methods of cointegration analysis. Two cointegration tests have been applied: Engle-Granger test and ADL test. The existence of the long-run relationship between the price level in the EMU (expressed in Croatian Kuna and the price level in the Republic of Croatia has been tested using monthly observations of average nominal Croatian Kuna exchange rate against Euro, Consumer Prices Index in the Republic of Croatia (2005=100 and the Harmonised Index of Consumer Prices for the European Monetary Union (2005=100 in the period from January 2000. to December 2012. Based on the Engle-Granger test, it can't be concluded if there is a long-run relationship between the two price levels. The non-existence of the long-run relationship between two price levels has also been confirmed by the ADL cointegration test. Thus, on the basis of the cointegration tests it can be concluded that the PPP hypothesis in the Republic of Croatia has not been confirmed.
A cointegration analysis of wine stock indexes
Directory of Open Access Journals (Sweden)
Sabina Introvigne
2017-12-01
Full Text Available This paper analyzes price patterns and long-run relationships for both fine wine and non-fine wine, with the aim to highlight price dynamics and co-movements between series, and to exploit potential diversification benefits. Data are from Liv-Ex 100 Fine Wine for fine wine, the Mediobanca Global Wine Industry Share Price for normal wine, and the MSCI World Index as a proxy of the overall stock market. Engle-Granger and Johansen tests were used to detect whether and to what extent the series co-move in the long run and which one of the variables contributes proactively to such an equilibrium by reacting to disequilibria from the long-run path. The estimates highlight that i the two wine indexes have a higher Sharpe ratio compared to the general stock market index, revealing wine stocks as a profitable investment per se, and ii the absence of cointegration among the three series and the existence of possible diversification benefits. In fact, in the long-run price do not move together and, therefore, investors may be better off by including wine stocks into investment portfolios and take advantage of diversification
Cointegration and Econometric Analysis of Non-Stationary Data in ...
African Journals Online (AJOL)
This is in conformity with the philosophy underlying the cointegration theory. Therefore, ignoring cointegration in non-stationary time series variables could lead to misspecification of the underlying process in the determination of corporate income tax in Nigeria. Thus, the study conclude that cointegration is greatly enhanced ...
Oil futures prices and stock management: a cointegration analysis
International Nuclear Information System (INIS)
Balabanoff, Stefan
1995-01-01
Futures markets are considered important to hedgers and speculators. Therefore, they are relevant to stock management. This issue is tested empirically by applying the methodology of cointegration analysis and causality testing to the monthly average of commercial (non-strategic) primary oil stocks and monthly averages of West Texas Intermediate (WTI) spot and futures prices for one month and three-months delivery, over the period January 1985 to June 1993. Long-and short-run relations are presented. The results support the view of a relationships between futures prices and oil stocks. (author)
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
Causality and cointegration analysis between macroeconomic variables and the Bovespa.
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Fabiano Mello da Silva
Full Text Available The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI, industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa. The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988 causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988 long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.
Causality and cointegration analysis between macroeconomic variables and the Bovespa.
da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes
2014-01-01
The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.
Cointegration analysis of wine export prices for France, Greece and Turkey
Mencet, M. Nisa; Firat, M. Ziya; Sayin, Cengiz
2006-01-01
Mediterranean countries have noticeable affect on the world wine exportation. Among these countries France, Greece and Turkey are selected for this study because of different wine market, trade systems and wine policies they have. In this study, cointegration analysis was conducted for real wine export prices and real exchange rates for France, Greece and Turkey. The long term relationships between real exchange rates and real wine export values were explored by using cointegration analysis. ...
Nonparametric factor analysis of time series
Rodríguez-Poo, Juan M.; Linton, Oliver Bruce
1998-01-01
We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.
Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm
Directory of Open Access Journals (Sweden)
Junbing Huang
2018-01-01
Full Text Available Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985–2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.
Global economic activity and crude oil prices. A cointegration analysis
International Nuclear Information System (INIS)
He, Yanan; Wang, Shouyang; Lai, Kin Keung
2010-01-01
This paper empirically investigates the cointegrating relationship between crude oil prices and global economic activity. The Kilian economic index is used as an indicator of global economic activity. Based on a supply-demand framework and the cointegration theory, we find that real futures prices of crude oil are cointegrated with the Kilian economic index and a trade weighted US dollar index, and crude oil prices are influenced significantly by fluctuations in the Kilian economic index through both long-run equilibrium conditions and short-run impacts. We also develop an empirically stable, data-coherent and single-equation error-correction model (ECM) which has sensible economic properties. Empirical results based on the ECM show that the adjustment implied by a permanent change in the Kilian economic index is a relatively drawn-out process. (author)
Nonparametric analysis of blocked ordered categories data: some examples revisited
Directory of Open Access Journals (Sweden)
O. Thas
2006-08-01
Full Text Available Nonparametric analysis for general block designs can be given by using the Cochran-Mantel-Haenszel (CMH statistics. We demonstrate this with four examples and note that several well-known nonparametric statistics are special cases of CMH statistics.
The demand for gasoline in South Africa. An empirical analysis using co-integration techniques
International Nuclear Information System (INIS)
Akinboade, Oludele A.; Ziramba, Emmanuel; Kumo, Wolassa L.
2008-01-01
Using the recently developed Autoregressive Distributed Lag (ARDL) bound testing approach to co-integration, suggested by Pesaran et al. (Pesaran, M.H., Shin, Y., Smith, R.J. Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics 2001; 16(3) 289-326), we empirically analyzed the long-run relationship among the variables in the aggregate gasoline demand function over the period 1978-2005. Our study confirms the existence of a co-integrating relationship. The estimated price and income elasticities of - 0.47 and 0.36 imply that gasoline demand in South Africa is price and income inelastic. (author)
The demand for gasoline in South Africa. An empirical analysis using co-integration techniques
Energy Technology Data Exchange (ETDEWEB)
Akinboade, Oludele A.; Ziramba, Emmanuel; Kumo, Wolassa L. [Department of Economics, University of South Africa, P.O.Box 392, Pretoria 0003 (South Africa)
2008-11-15
Using the recently developed Autoregressive Distributed Lag (ARDL) bound testing approach to co-integration, suggested by Pesaran et al. (Pesaran, M.H., Shin, Y., Smith, R.J. Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics 2001; 16(3) 289-326), we empirically analyzed the long-run relationship among the variables in the aggregate gasoline demand function over the period 1978-2005. Our study confirms the existence of a co-integrating relationship. The estimated price and income elasticities of - 0.47 and 0.36 imply that gasoline demand in South Africa is price and income inelastic. (author)
Bank Credit and Aggregate Import Demand in Nigeria: A Cointegration Analysis
Directory of Open Access Journals (Sweden)
Philip Chimobi Omoke
2012-06-01
Full Text Available This study reformulated the aggregate import demand for Nigeria by including a financial variable (bank credit into the traditional import demand function for the period 1970-2009. The Johansen Multivariate cointegration analysis was used to estimate the function. The result obtained from the study shows no evidence of the existence of cointegrating relations between bank credit and import demand. This shows that bank credit is found to be insufficient as a policy instrument for long term import demand in Nigeria. Thus, the financial variable should not be included in modelling the aggregate import demand for Nigeria.
A Bayesian Nonparametric Approach to Factor Analysis
DEFF Research Database (Denmark)
Piatek, Rémi; Papaspiliopoulos, Omiros
2018-01-01
This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...
Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs
Kuosmanen, T.K.
2005-01-01
Environmental Economics and Natural Resources Group at Wageningen University in The Netherlands Weak disposability of outputs means that firms can abate harmful emissions by decreasing the activity level. Modeling weak disposability in nonparametric production analysis has caused some confusion.
Bivariate Cointegration Analysis of Energy-Economy Interactions in Iran
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Ismail Oladimeji Soile
2015-12-01
Full Text Available Fixing the prices of energy products below their opportunity cost for welfare and redistribution purposes is common with governments of many oil producing developing countries. This has often resulted in huge energy consumption in developing countries and the question that emerge is whether this increased energy consumption results in higher economic activities. Available statistics show that Iran’s economy growth shrunk for the first time in two decades from 2011 amidst the introduction of pricing reform in 2010 and 2014 suggesting a relationship between energy use and economic growth. Accordingly, the study examined the causality and the likelihood of a long term relationship between energy and economic growth in Iran. Unlike previous studies which have focused on the effects and effectiveness of the reform, the paper investigates the rationale for the reform. The study applied a bivariate cointegration time series econometric approach. The results reveals a one-way causality running from economic growth to energy with no feedback with evidence of long run connection. The implication of this is that energy conservation policy is not inimical to economic growth. This evidence lend further support for the ongoing subsidy reforms in Iran as a measure to check excessive and inefficient use of energy.
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...
Caruso, Raul; Di Domizio, Marco
2015-01-01
The aim of this paper is to study the interdependence of military spending between US and a panel of European countries in the period 1988-2013. The empirical estimation is based on a: (i) a unit root tests and a cointegration analysis; (ii) FMOLS and DOLS estimations. General results highlight that military spending of European countries is: (1) positively associated with US military spending and (2) negatively associated with average military spending of other European countries.
Energy Consumption and Economic Growth in Algeria: Cointegration and Causality Analysis
Directory of Open Access Journals (Sweden)
Cherfi Souhila
2012-01-01
Full Text Available This study investigates the energy consumption-growth nexus in Algeria. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1965-2008 period is examined using the threshold cointegration and Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Algeria are non cointegrated and that there is a uni-directional causality running from LPCGDP to LPCEC, but not vice versa. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Algeria. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. The energy should be efficiently allocated into more productive sectors of the economy.
Non-parametric analysis of production efficiency of poultry egg ...
African Journals Online (AJOL)
Non-parametric analysis of production efficiency of poultry egg farmers in Delta ... analysis of factors affecting the output of poultry farmers showed that stock ... should be put in place for farmers to learn the best farm practices carried out on the ...
A Bayesian Nonparametric Meta-Analysis Model
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.
2015-01-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…
The co-integration analysis of factors affecting electricity consumption : a case study of Thailand
Energy Technology Data Exchange (ETDEWEB)
Kongruang, C. [Thaksin Univ., Songkhla (Thailand). Faculty of Economics and Business; Waewsak, J. [Thaksin Univ., Phatthalung (Thailand). Dept. of Physics, Solar and Wind Energy Research Lab
2008-07-01
A study was conducted in which the main determinants of electricity demand in Thailand were investigated. Time-series analysis methods were used, notably the unit root test, the Johansen co-integration test and an error correction model (ECM). The objective was to determine the factors affecting short and long-run electricity consumption. This paper presented annual time series data from 1971 to 2006. The unit root test revealed that all series are non-stationary. The Johansen co-integration test revealed the co-integration between variables and the existence of the long-term relationship between them. Electricity consumption accelerated with the increase in gross domestic product (GDP) and population. In contrast, an increase in commodity price would reduce electricity consumption. The coefficient of population indicated that an increase of 1 million in total population would result in an increase in electricity consumption of 0.099 per cent. Last, the results of ECM revealed that nearly 21 per cent of long-term disequilibrium is adjusted to the current period. The factors that affect electricity consumption include GDP growth, inflation rate and population growth. It was concluded that Thailand should prepare for additional power generation from clean energy sources such as solar, biomass and wind energy. Thailand's Energy Policy and Planning Office revealed that electricity consumption in 2008 would be over 130,000 GWh with per capita consumption at nearly 2,000 kWh. The power peak demand will be nearly 24,000 MW in 2008. This result was based on the forecasting model that considered only GDP growth. However, electricity consumption depends not only on the GDP growth, but also upon the other key variables such as population, electricity selling price, consumer price index and temperature. 17 refs., 5 tabs., 4 figs.
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
2012-01-01
by investigating the relationship between the elasticity of scale and the farm size. We use a balanced panel data set of 371~specialised crop farms for the years 2004-2007. A non-parametric specification test shows that neither the Cobb-Douglas function nor the Translog function are consistent with the "true......Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify a functional form of the production function of which the Cobb...... parameter estimates, but also in biased measures which are derived from the parameters, such as elasticities. Therefore, we propose to use non-parametric econometric methods. First, these can be applied to verify the functional form used in parametric production analysis. Second, they can be directly used...
Eco taxes and double dividend. A co-integration analysis
International Nuclear Information System (INIS)
Ghignoni, E.
1999-01-01
The paper tackles with the critical discussion of the eco tax double dividend theory. It is built up a theoretical and empirical analysis on short and long run effects of changes in real prices of energetic inputs on the use of polluted sources and labour, as well as on short and long run effects of changes in real labour cost on the use of labour and energy. At that purpose Johansen's procedure allows to get a structural multivariate model of dynamic relationships among the variables enclosed in the data set of a production function with labour and energy. This model is submitted to a dynamic simulation and results suggest that the introduction of an eco tax coupled with a decreasing in real labour cost would have uncertain and not leasing effects on the reduction of pollution coming from the utilisation of energetic sources and very limited effects on employment [it
Statistical analysis of hypotheses on the cointegrating relations in the I(2) model
DEFF Research Database (Denmark)
Johansen, Søren
2006-01-01
The cointegrated vector autoregressive model for I(2) variables is a non-linear parametric restriction on the linear I(2) regression model for variables of order I(0), I(1) and I(2). In this paper we discuss non-linear submodels given by smooth parametrizations. We give conditions on the parametr......) and the reformulation is applied to show that some hypotheses on the cointegrating coefficients in the cointegrated I(2) model give asymptotic ¿² inference....
Digital spectral analysis parametric, non-parametric and advanced methods
Castanié, Francis
2013-01-01
Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a
Cointegration analysis for rice production in the states of Perlis and Johor, Malaysia
Shitan, Mahendran; Ng, Yung Lerd; Karmokar, Provash Kumar
2015-02-01
Rice is ranked the third most important crop in Malaysia after rubber and palm oil in terms of production. Unlike the industrial crops, although its contribution to Malaysia's economy is minimal, it plays a pivotal role in the country's food security as rice is consumed by almost everyone in Malaysia. Rice production is influenced by factors such as geographical location, temperature, rainfall, soil fertility, farming practices, etc. and hence the productivity of rice may differ in different state. In this study, our particular interest is to investigate the interrelationship between the rice production of Perlis and Johor. Data collected from Department of Agriculture, Government of Malaysia are tested for unit roots by Augmented Dickey-Fuller (ADF) unit root test while Engle-Granger (EG) procedure is used in the cointegration analysis. Our study shows that cointegrating relationship exists among the rice production in both states. The speed of adjustment coefficient of the error correction model (ECM) of Perlis is 0.611 indicating that approximately 61.1% of any deviation from the long-run path is corrected within a year by the production of rice in Johor.
Energy Consumption and Economic Growth in Vietnam: Threshold Cointegration and Causality Analysis
Directory of Open Access Journals (Sweden)
BINH Thanh PHUNG
2011-01-01
Full Text Available This study investigates the energy consumption-growth nexus in Vietnam. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1976-2010 period is examined using the threshold cointegration and vector error correction models for Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Vietnam are cointegrated and that there is a strong uni-directional causality running from LPCGDP to LPCEC, but not vice versa. It is also found that the effect of LPCGDP on LPCEC in Vietnam is time-varying (i.e. significantly different between before and after the structural breakpoint, 1992. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Vietnam. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. In future, the energy should be efficiently allocated into more productive sectors of the economy.
Education and Economic Growth in Pakistan: A Cointegration and Causality Analysis
Afzal, Muhammad; Rehman, Hafeez Ur; Farooq, Muhammad Shahid; Sarwar, Kafeel
2011-01-01
This study explored the cointegration and causality between education and economic growth in Pakistan by using time series data on real gross domestic product (RGDP), labour force, physical capital and education from 1970-1971 to 2008-2009 were used. Autoregressive Distributed Lag (ARDL) Model of Cointegration and the Augmented Granger Causality…
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...
Using non-parametric methods in econometric production analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb...... results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used...
STATCAT, Statistical Analysis of Parametric and Non-Parametric Data
International Nuclear Information System (INIS)
David, Hugh
1990-01-01
1 - Description of program or function: A suite of 26 programs designed to facilitate the appropriate statistical analysis and data handling of parametric and non-parametric data, using classical and modern univariate and multivariate methods. 2 - Method of solution: Data is read entry by entry, using a choice of input formats, and the resultant data bank is checked for out-of- range, rare, extreme or missing data. The completed STATCAT data bank can be treated by a variety of descriptive and inferential statistical methods, and modified, using other standard programs as required
Statistical analysis of global surface temperature and sea level using cointegration methods
DEFF Research Database (Denmark)
Schmidt, Torben; Johansen, Søren; Thejll, Peter
2012-01-01
Global sea levels are rising which is widely understood as a consequence of thermal expansion and melting of glaciers and land-based ice caps. Due to the lack of representation of ice-sheet dynamics in present-day physically-based climate models being unable to simulate observed sea level trends......, semi-empirical models have been applied as an alternative for projecting of future sea levels. There is in this, however, potential pitfalls due to the trending nature of the time series. We apply a statistical method called cointegration analysis to observed global sea level and land-ocean surface air...... temperature, capable of handling such peculiarities. We find a relationship between sea level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. We further find that the warming episode in the 1940s...
Statistical analysis of global surface air temperature and sea level using cointegration methods
DEFF Research Database (Denmark)
Schmith, Torben; Johansen, Søren; Thejll, Peter
Global sea levels are rising which is widely understood as a consequence of thermal expansion and melting of glaciers and land-based ice caps. Due to physically-based models being unable to simulate observed sea level trends, semi-empirical models have been applied as an alternative for projecting...... of future sea levels. There is in this, however, potential pitfalls due to the trending nature of the time series. We apply a statistical method called cointegration analysis to observed global sea level and surface air temperature, capable of handling such peculiarities. We find a relationship between sea...... level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. We further find that the warming episode in the 1940s is exceptional in the sense that sea level and warming deviates from the expected...
Price and income elasticities of crude oil import demand in South Africa. A cointegration analysis
Energy Technology Data Exchange (ETDEWEB)
Ziramba, Emmanuel [Department of Economics, University of South Africa, P.O Box 392, Unisa 0003 (South Africa)
2010-12-15
This paper examines the demand for imported crude oil in South Africa as a function of real income and the price of crude oil over the period 1980-2006. We carried out the Johansen co integration multivariate analysis to determine the long-run income and price elasticities. A unique long-run cointegration relationship exists between crude oil imports and the explanatory variables. The short-run dynamics are estimated by specifying a general error correction model. The estimated long-run price and income elasticities of -0.147 and 0.429 suggest that import demand for crude oil is price and income inelastic. There is also evidence of unidirectional long-run causality running from real GDP to crude oil imports. (author)
International Nuclear Information System (INIS)
Jahangir Alam, Mohammad; Ara Begum, Ismat; Buysse, Jeroen; Van Huylenbroeck, Guido
2012-01-01
The paper investigates the possible existence of dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth in Bangladesh. First, we have tested cointegration relationships using the Johansen bi-variate cointegration model. This is complemented with an analysis of an auto-regressive distributed lag model to examine the results' robustness. Then, the Granger short-run, the long-run and strong causality are tested with a vector error correction modelling framework. The results indicate that uni-directional causality exists from energy consumption to economic growth both in the short and the long-run while a bi-directional long-run causality exists between electricity consumption and economic growth but no causal relationship exists in short-run. The strong causality results indicate bi-directional causality for both the cases. A uni-directional causality runs from energy consumption to CO 2 emission for the short-run but feedback causality exists in the long-run. CO 2 Granger causes economic growth both in the short and in the long-run. An important policy implication is that energy (electricity as well) can be considered as an important factor for the economic growth in Bangladesh. Moreover, as higher energy consumption also means higher pollution in the long-run, policy makers should stimulate alternative energy sources for meeting up the increasing energy demand. - Highlights: ► Dynamic causality among energy and electricity consumption, CO 2 and economic growth. ► Uni-directional causality exists from energy consumption to economic growth. ► Bi-directional causality exists between electricity consumption and economic growth. ► Feedback causality exists between CO 2 emission to energy consumption. ► CO 2 Granger causes economic growth both in the short and in the long-run.
Wesselink, Christiaan; Heeg, Govert P.; Jansonius, Nomdo M.
Objective: To compare prospectively 2 perimetric progression detection algorithms for glaucoma, the Early Manifest Glaucoma Trial algorithm (glaucoma progression analysis [GPA]) and a nonparametric algorithm applied to the mean deviation (MD) (nonparametric progression analysis [NPA]). Methods:
Multi-Directional Non-Parametric Analysis of Agricultural Efficiency
DEFF Research Database (Denmark)
Balezentis, Tomas
This thesis seeks to develop methodologies for assessment of agricultural efficiency and employ them to Lithuanian family farms. In particular, we focus on three particular objectives throughout the research: (i) to perform a fully non-parametric analysis of efficiency effects, (ii) to extend...... to the Multi-Directional Efficiency Analysis approach when the proposed models were employed to analyse empirical data of Lithuanian family farm performance, we saw substantial differences in efficiencies associated with different inputs. In particular, assets appeared to be the least efficiently used input...... relative to labour, intermediate consumption and land (in some cases land was not treated as a discretionary input). These findings call for further research on relationships among financial structure, investment decisions, and efficiency in Lithuanian family farms. Application of different techniques...
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
Jinchao Li; Lin Chen; Yuwei Xiang; Jinying Li; Dong Peng
2018-01-01
Electric grid investment demand analysis is significant to reasonably arranging construction funds for the electric grid and reduce costs. This paper used the panel data of electric grid investment from 23 provinces of China between 2004 and 2016 as samples to analyze the influence between electric grid investment demand and GDP, population scale, social electricity consumption, installed electrical capacity, and peak load based on co-integration tests. We find that GDP and peak load have pos...
Discrete non-parametric kernel estimation for global sensitivity analysis
International Nuclear Information System (INIS)
Senga Kiessé, Tristan; Ventura, Anne
2016-01-01
This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Fractional cointegration rank estimation
DEFF Research Database (Denmark)
Lasak, Katarzyna; Velasco, Carlos
the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p-1. This step provides consistent estimates of the cointegration degree, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup......-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the null. The cointegration degree is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step...... to control for stochastic trend estimation effects from the first step. The critical values of the tests proposed depend only on the number of common trends under the null, p - r, and on the interval of the cointegration degrees b allowed, but not on the true cointegration degree b0. Hence, no additional...
Reassessing the integration of European electricity markets: A fractional cointegration analysis
International Nuclear Information System (INIS)
Menezes, Lilian M. de; Houllier, Melanie A.
2016-01-01
This study extends existing literature on the assessment of electricity market integration in Europe, by developing and testing hypotheses on the convergence of electricity wholesale prices, and adopting a time-varying fractional cointegration analysis. In addition, the potential impacts of some special events that may affect system capacity (new interconnection, market coupling, increase in share of intermittent generation) on spot and forward markets are considered and evaluated. Daily spot prices from February 2000 to March 2013 of nine European electricity spot markets (APX-UK, APX-NL, Belpex, EPEX-FR, EPEX-DE, IPEX, Nordpool, Omel and OTE) and month-ahead prices in four markets (French, British, German and Dutch) from November 2007 to December 2012 are investigated. Results show that unit root tests, which are generally used in the literature to test market integration, are inadequate for assessing electricity spot market convergence, because spot prices are found to be fractionally integrated and mean-reverting time series. Furthermore, spot price behaviour and their association with different markets change over time, reflecting changes in the EU electrical system. One-month-ahead prices, by contrast, were found to have become more resilient to shocks and to follow more stable trends. - Highlights: • We examine electricity market convergence in the EU. • Common price dynamics are affected by changes in interconnection and capacity. • Forward markets have increased in resilience. • Germany's nuclear plant closures had an adverse effect on most European electricity markets.
Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey
International Nuclear Information System (INIS)
Erdogdu, Erkan
2007-01-01
In the early 2000s, the Republic of Turkey has initiated an ambitious reform program in her electricity market, which requires privatization, liberalization as well as a radical restructuring. The most controversial reason behind, or justification for, recent reforms has been the rapid electricity demand growth; that is to say, the whole reform process has been a part of the endeavors to avoid the so-called 'energy crisis'. Using cointegration analysis and autoregressive integrated moving average (ARIMA) modelling, the present article focuses on this issue by both providing an electricity demand estimation and forecast, and comparing the results with official projections. The study concludes, first, that consumers' respond to price and income changes is quite limited and therefore there is a need for economic regulation in Turkish electricity market; and second, that the current official electricity demand projections highly overestimate the electricity demand, which may endanger the development of both a coherent energy policy in general and a healthy electricity market in particular
Price and income elasticities of crude oil import demand in South Africa: A cointegration analysis
Energy Technology Data Exchange (ETDEWEB)
Ziramba, Emmanuel, E-mail: zirame@unisa.ac.z [Department of Economics, University of South Africa, P.O Box 392, Unisa 0003 (South Africa)
2010-12-15
This paper examines the demand for imported crude oil in South Africa as a function of real income and the price of crude oil over the period 1980-2006. We carried out the Johansen co integration multivariate analysis to determine the long-run income and price elasticities. A unique long-run cointegration relationship exists between crude oil imports and the explanatory variables. The short-run dynamics are estimated by specifying a general error correction model. The estimated long-run price and income elasticities of -0.147 and 0.429 suggest that import demand for crude oil is price and income inelastic. There is also evidence of unidirectional long-run causality running from real GDP to crude oil imports. - Research Highlights: {yields}The paper examines the demand for imported crude oil in South Africa over the period 1980-2006. {yields} The estimated long-run price and income elasticities are -0.147 and 0.429, respectively. {yields} There is evidence of unidirectional long-run causality running from real GDP to crude oil imports.
Directory of Open Access Journals (Sweden)
Khalid Zaman
2012-06-01
Full Text Available This paper attempts to examine the relationship between real military spending (RME, level of economic activity (RGNP, and real external debt (RED by using a Johansen multivariate cointegration framework. The analysis is carried out using time series data over 1980-2008 The study investigates the long-run effects and short-run dynamics of the effect of rise in RGNP and RME on RED Pakistan. The quantitative evidence shows that external debt is more elastic with respect to military expenditure in the long run, whereas, there has been insignificant effect in the short-run. In the long-run, 1.00% increase in military expenditure leads to an increase in external debt by almost 3.96%. On the other hand, 1.00% increases in economic growth decreases external debt by 2.13%. In the short run, 1.00% increase in economic growth reduces external debt by 2.90%. The results presented in this study reinforce the importance to government, academic, and policy makers.
Price and income elasticities of crude oil import demand in South Africa: A cointegration analysis
International Nuclear Information System (INIS)
Ziramba, Emmanuel
2010-01-01
This paper examines the demand for imported crude oil in South Africa as a function of real income and the price of crude oil over the period 1980-2006. We carried out the Johansen co integration multivariate analysis to determine the long-run income and price elasticities. A unique long-run cointegration relationship exists between crude oil imports and the explanatory variables. The short-run dynamics are estimated by specifying a general error correction model. The estimated long-run price and income elasticities of -0.147 and 0.429 suggest that import demand for crude oil is price and income inelastic. There is also evidence of unidirectional long-run causality running from real GDP to crude oil imports. - Research Highlights: →The paper examines the demand for imported crude oil in South Africa over the period 1980-2006. → The estimated long-run price and income elasticities are -0.147 and 0.429, respectively. → There is evidence of unidirectional long-run causality running from real GDP to crude oil imports.
Economic openness and economic growth: A cointegration analysis for ASEAN-5 countries
Directory of Open Access Journals (Sweden)
Klimis Vogiatzoglou
2016-11-01
Full Text Available The paper considers three channels of economic openness, namely FDI, imports, and exports, and examines their short-run and long-run effects on the economic growth in the five founding member countries of the Association of Southeast Asian Nations (ASEAN over the period from 1980 to 2014. Besides the impact on the economic growth, the authors analyze all possible causal interrelationships to discern patterns and directions of causality among FDI, imports, exports, and GDP. The quantitative analysis, which is based on the vector error correction co-integration framework, is conducted separately for each country in order to assess their individual experiences and allow for a comparative view. Although the precise details differ across countries, the findings indicate that there is a long-run equilibrium relationship between economic openness and GDP in all ASEAN-5 economies. FDI, imports and exports have a significantly positive short-run and long-run impact on the economic growth. Our results also show that export-led growth is the most important economic growth factor in most countries, followed by FDI-led growth. Another crucial finding is the bi-directional causality between exports and FDI across the ASEAN-5 countries. This indicates the presence of direct and indirect effects on GDP and a self-reinforcing process of causality between those two variables, which strengthens their impact on the economic growth.
Cointegration analysis on trading behavior in four SELECTED asean countries BEFORE MONETARY CRISIS
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R. Budi Prawoto
2007-06-01
Full Text Available This paper aims to analyze Indonesian position among the trading behavior in four selected ASEAN countries (according to their import-and-export products using cointegration analysis. The demands for export and import are estimated before the monetary crisis erupted (1963 – 1995 using the dynamic OLS (DOLS method. The Johansen Maximum Likelihood (JML approach is also employed to compare the results obtained. The results show that foreign income has a significant impact on export demand, suggesting that foreign disturbance in the form of economic activities is likely to be transmitted to these countries. The Marshall Lerner conditions are easily met for the cases of Malaysia and Thailand (DOLS and JML. For Indonesia and the Philippines, the sum of the price elasticities of export and import demand are less than unity. This can be explained by the J-curve, in which the currency depreciations will first worsen the trade balance before it improves, and it takes a long time to affect the trade balance.
Directory of Open Access Journals (Sweden)
Yilmaz Bayar
2017-09-01
Full Text Available Purpose: The goal of the paper is to investigate the long run effect of both foreign direct investments and domestic investments on the unemployment in 21 emerging economies over the period 1994-2014. Design/methodology/approach: The effect of domestic and foreign direct investments on unemployment was investigated via panel data analysis. First tests of cross-section dependence and homogeneity were conducted, and then the stationarity of the series was analyzed with Pesaran's (2007 CIPS unit root test. The long run relationship among the series was examined with Westerlund-Durbin-Hausman's (2008 co -integration test. Finally, we estimated the long run coefficients with the Augmented Mean Group (AMG estimator. Findings: The empirical findings revealed a co-integrating relationship among domestic investments, foreign direct investments, and unemployment. Furthermore, foreign direct investment inflows affected the unemployment positively in the long term, but domestic investments affected the unemployment negatively. Originality/value: This study can be considered as one of the early studies researching the long run interaction between domestic investments, foreign direct investments and unemployment for the sample of emerging market economies. Furthermore, the findings are very meaningful for policymakers in the design the economic policies for decreasing unemployment.
Multivariate co-integration analysis of the Kaya factors in Ghana.
Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa
2016-05-01
The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.
Disaggregated energy consumption and GDP in Taiwan: A threshold co-integration analysis
International Nuclear Information System (INIS)
Hu, J.-L.; Lin, C.-H.
2008-01-01
Energy consumption growth is much higher than economic growth for Taiwan in recent years, worsening its energy efficiency. This paper provides a solid explanation by examining the equilibrium relationship between GDP and disaggregated energy consumption under a non-linear framework. The threshold co-integration test developed with asymmetric dynamic adjusting processes proposed by Hansen and Seo [Hansen, B.E., Seo, B., 2002. Testing for two-regime threshold cointegration in vector error-correction models. Journal of Econometrics 110, 293-318.] is applied. Non-linear co-integrations between GDP and disaggregated energy consumptions are confirmed except for oil consumption. The two-regime vector error-correction models (VECM) show that the adjustment process of energy consumption toward equilibrium is highly persistent when an appropriately threshold is reached. There is mean-reverting behavior when the threshold is reached, making aggregate and disaggregated energy consumptions grow faster than GDP in Taiwan
Wall, Martin; Casswell, Sally
2013-01-01
To investigate whether affordability of alcohol is an important determinant of alcohol consumption along with price. This will inform effective tax policy to influence consumption. Co-integration analysis was used to analyse relationship between real price, affordability and consumption. Changes in retail availability of wine in 1990 and beer in 1999 were also included in the models. The econometric approach taken allows identification of short- and long-term responses. Separate analyses were performed for wine, beer, spirits and ready-to-drinks (spirits based pre-mixed drinks). New Zealand 1988-2011. Quarterly data on price and alcohol available for consumption for wine, beer, spirits and ready-to-drinks. Price data were analysed as: real price (own price of alcohol relative to the price of other goods) and affordability (average earnings relative to own price). There was strong evidence for co-integration between wine and beer consumption and affordability. There was weaker evidence for co-integration between consumption and real price. The affordability of alcohol is more important than real price in determining consumption of alcohol. This suggests that affordability needs to be considered by policy makers when determining tax and pricing policies to reduce alcohol-related harm. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.
STRUCTURAL BREAKS, COINTEGRATION, AND CAUSALITY BY VECM ANALYSIS OF CRUDE OIL AND FOOD PRICE
Directory of Open Access Journals (Sweden)
Aynur Pala
2013-01-01
Full Text Available This papers investigated form of the linkage beetwen crude oil price index and food price index, using Johansen Cointegration test, and Granger Causality by VECM. Empirical results for monthly data from 1990:01 to 2011:08 indicated that evidence for breaks after 2008:08 and 2008:11. We find a clear long-run relationship between these series for the full and sub sample. Cointegration regression coefficient is negative at the 1990:01-2008:08 time period, but adversely positive at the 2008:11-2011:08 time period. This results represent that relation between crude oil and food price chanced.
Directory of Open Access Journals (Sweden)
Aldrin Herwany
2008-09-01
This study assesses the cointegration and causal relations among seven developed Asian markets, i.e., Tokyo, Hong Kong, Korea, Taiwan, Shanghai, Singapore, and Kuala Lumpur stock exchanges, using more frequent time series data. It employs the recently developed techniques for investigating unit roots, cointegration, time-varying volatility, and causality in variance. For estimating portfolio market risk, this study employs Value-at-Risk with delta normal approach. The results would recommend whether fund managers are able to diversify their portfolio in these developed stock markets either in long run or in short run.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...
Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey
Erdem, Ekrem; Tugcu, Can Tansel
2012-01-01
This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…
A PANEL COINTEGRATION ANALYSIS: AN APPLICATION TO INTERNATIONAL TOURISM DEMAND OF THAILAND
Directory of Open Access Journals (Sweden)
CHUKIAT CHAIBOONSRI
2010-01-01
Full Text Available This paper sought to find the long-run relationships between international tourist arrivals in Thailand and economic variables such as GDP, transportation cost and exchange rates during period of 1986 to 2007. Also this paper used five standard panel unit root tests such as LLC (2002 panel unit root test, Breitung (2000 panel unit root test, IPS (2003 panel unit root test, Maddala and Wu (1999 and Choi (2001 panel unit root test and Handri (1999 panel unit root test. Moreover, the panel cointegration test based on Pedroni residual cointegration tests, Kao residual cointegration tests and Johansen fisher panel cointegration test were used to test in panel among the variables. The OLS estimator, DOLS estimator and FMOLS estimator were used to find the long-run relationship of the international tourism demand model for Thailand.The long-run results indicated that growth in income (GDP of Thai’s Asia major tourist source markets (Malaysia, Japan, Korea, China, Singapore and Taiwan have a positive impact on international tourists arrival to Thailand. In addition, the transportation cost of these countries has negative impact on the number of international tourist arrivals to Thailand. Finally Thailand’s currency has positive impact on the number of international tourist arrivals to Thailand. Most of findings from this study were consistent with economic theory and the implications of the model can be use for policy making.
The analysis of nonstationary time series using regression, correlation and cointegration
DEFF Research Database (Denmark)
Johansen, Søren
2012-01-01
There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we...... analyse some monthly data from US on interest rates as an illustration of the methods...
Cointegration analysis of the dynamic Nelson-Siegel model using the wild bootstrap
Boswijk, H.P.
2013-01-01
The Dynamic Nelson-Siegel model describes the evolution over time of the term structure of interest rates in terms of three factors, characterising the level, slope and curvature of the yield curve. This article uses recently developed cointegration techniques for heteroskedastic time series to
The Analysis of Nonstationary Time Series Using Regression, Correlation and Cointegration
Directory of Open Access Journals (Sweden)
Søren Johansen
2012-06-01
Full Text Available There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference using the cointegrated vector autoregressive model. Finally we analyse some monthly data from US on interest rates as an illustration of the methods.
Non-Parametric Analysis of Rating Transition and Default Data
DEFF Research Database (Denmark)
Fledelius, Peter; Lando, David; Perch Nielsen, Jens
2004-01-01
We demonstrate the use of non-parametric intensity estimation - including construction of pointwise confidence sets - for analyzing rating transition data. We find that transition intensities away from the class studied here for illustration strongly depend on the direction of the previous move b...
Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A
2017-06-30
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
DEMAND FOR OIL PRODUCTS IN OPEC COUNTRIES: A PANEL COINTEGRATION ANALYSIS
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Nourah Al Yousef
2013-01-01
Full Text Available The increasing consumption of oil-refined products on OPEC countries will have its impact on the availability of oil exports. The goal of this paper is to examine the determinants of oil refined products’ consumption for a panel consisting of 7 OPEC countries, namely, Algeria, Kuwait, Libya, Qatar, Saudi Arabia, United Emirates and Iran for the period of 1980–2010, by employing the recently developed panel data unit root tests and panel data cointegration techniques. Furthermore, conditional on finding cointegration, the paper extends the literature by employing the Pedroni Panel Fully Modified Ordinary Least Squares (FMOLS Dynamic OLS (DOLS procedure to generate. The study estimates the demand for Gasoline, Kerosene and Diesel. An attempt is also made to assess the impact of this demand on the future availability of OPEC oil exports.
International Nuclear Information System (INIS)
Siliverstovs, Boriss; L'Hegaret, Guillaume; Neumann, Anne; Hirschlausen, Christian von
2005-01-01
This paper investigates the degree of integration of natural gas markets in Europe, North America and Japan in the time period between the early 1990s and 2004. The relationship between international gas market prices and their relation to the oil price are explored through principal components analysis and Johansen likelihood-based cointegration procedure. Both of them show a high level of natural gas market integration within Europe, between the European and Japanese markets as well as within the North American market. At the same time the obtained results suggest that the European (respectively, Japanese) and the North American markets were not integrated. (Author)
An empirical analysis of gasoline demand in Denmark using cointegration techniques
International Nuclear Information System (INIS)
Bentzen, Jan
1994-01-01
Danish time-series data covering the period 1948-91 are used in order to estimate short-run and long-run elasticities in gasoline demand. A cointegration test for a stable long-run relationship between the variables in the model proves to be positive, showing a smaller value of the long-run price elasticity than often quoted in empirical studies of gasoline demand. Finally, an error correction model is estimated. (author)
Colombian equity return and narrow money supply: an asymmetric cointegration analysis
Chu V. Nguyen
2012-01-01
The asymmetric, cointegrating relationship between the return on equity market and the narrowly defined money supply is documented. In fact, equity return and the monthly percentage change in the Colombian money supply M1 spread adjusts to the threshold value slower when a contractionary countercyclical policy action or an economic shock causes the money supply M1 to fall relative to the share price index, widening their spread, than when an expansionary countercyclical monetary policy action...
The Export Supply Response ofMangoes: A Cointegration and Causality Analysis
Abdul Ghafoor; Khalid Mushtaq; Abedullah
2013-01-01
This paper analyzes the impact of major factors on the export of mangoes from Pakistan. We use a cointegration approach and error correction mechanism applied to data for the period 1970–2005. Mango exports are regressed against the index of relative prices of mango exports (2000 = 100), the quantity of domestic mango production, real agricultural gross domestic product (GDP), the length of all-weather roads, and international standardization, i.e., the impact of the World Trade Organization ...
International Nuclear Information System (INIS)
Baek, Jungho
2015-01-01
Highlights: • This study revisits the nuclear-energy-growth-CO 2 emissions nexus. • A panel cointegration analysis is employed. • Nuclear energy has a beneficial effect on reducing CO 2 emissions. • CO 2 emissions decrease with economic growth. - Abstract: A number of studies have examined the effect of nuclear energy on CO 2 emissions, and a lot has been learned from these studies. Due to their weaknesses in modeling approaches and variable uses, however, properly constructed and comprehensive analyses are limited. The main objective of this study is thus to contribute to the debate over nuclear energy and the environment with an enhanced model and variables. For this, a panel cointegration analysis is applied to quantify the effects of nuclear energy, energy consumption and income on CO 2 emissions in 12 major nuclear generating countries. The results show that nuclear energy tends to reduce CO 2 emissions. It is also found that CO 2 emissions tend to decrease monotonically with income growth, providing no evidence in support of the Environmental Kuznets Curve (EKC) for CO 2 emissions
Categorical and nonparametric data analysis choosing the best statistical technique
Nussbaum, E Michael
2014-01-01
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain
Ghassan, Hassan B.; Banerjee, Prashanta K.
2013-01-01
The purpose of this paper is to analyze the dynamics of crude oil prices of OPEC and non-OPEC countries using threshold cointegration. To capture the long run asymmetric price transmission mechanism, we develop an error correction model within a threshold cointegration and CGARCH errors framework. The empirical contribution of our paper specifies the cointegrating relation between OPEC price and non-OPEC prices and estimates how and to what extent the respective prices adjust to eliminate dis...
International Nuclear Information System (INIS)
Narayan, Paresh Kumar; Smyth, Russell; Prasad, Arti
2007-01-01
This article applies recently developed panel unit root and panel cointegration techniques to estimate the long-run and short-run income and price elasticities for residential demand for electricity in G7 countries. The panel results indicate that in the long-run residential demand for electricity is price elastic and income inelastic. The study concludes that from an environmental perspective there is potential to use pricing policies in the G7 countries to curtail residential electricity demand, and thus curb carbon emissions, in the long run. (author)
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction
Directory of Open Access Journals (Sweden)
Helmut Thome
2015-07-01
Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.
A panel cointegration analysis of the demand for oil in the Middle East
International Nuclear Information System (INIS)
Narayan, Paresh Kumar; Smyth, Russell
2007-01-01
This article applies recently developed panel unit root and panel cointegration techniques to estimate the long-run income and price elasticities for oil in the Middle East. The results for the panel indicate that demand for oil is highly price inelastic and slightly income elastic in the Middle East. There is considerable variation in the results for the income variable across countries, with the coefficient on the income variable statistically insignificant for several countries. The coefficient on the price variable is statistically significant in all cases with the expected sign and the price elasticity is uniformly low. While the results for the income variable differ across countries, the results for the panel as a whole suggest that the demand for oil in the Middle East is being driven largely by strong economic growth, while consumers are largely insensitive to price changes
The law of one price in global natural gas markets. A threshold cointegration analysis
Energy Technology Data Exchange (ETDEWEB)
Nick, Sebastian; Tischler, Benjamin
2014-11-15
The US and UK markets for natural gas are connected by arbitrage activity in the form of shifting trade volumes of liquefied natural gas (LNG). We empirically investigate the degree of integration between the US and the UK gas markets by using a threshold cointegration approach that is in accordance with the law of one price and explicitly accounts for transaction costs. Our empirical results reveal a high degree of market integration for the period 2000-2008. Although US and UK gas prices seemed to have decoupled between 2009 and 2012, we still find a certain degree of integration pointing towards significant regional price arbitrage. However, high threshold estimates in the latter period indicate impediments to arbitrage that are by far surpassing the LNG transport costs difference between the US and UK gas market.
Systematic Risk Factors for Australian Stock Market Returns: a Cointegration Analysis
Directory of Open Access Journals (Sweden)
Mazharul H. Kazi
2008-12-01
Full Text Available This paper identifies the systematic risk factors for the Australian stock market by applyingthe cointegration technique of Johansen. In conformity with the finance literature andinvestors’ common intuition, relevant a priori variables are chosen to proxy for Australiansystematic risk factors. The results show that only a few systematic risk factors are dominantfor Australian stock market price movements in the long-run while short-run dynamics are inplace. It is observed that the linear combination of all a priori variables is cointegratedalthough not all variables are significantly influential. The findings show that bank interestrate, corporate profitability, dividend yield, industrial production and, to a lesser extent, globalmarket movements are significantly influencing the Australian stock market returns in thelong-run; while in the short-run it is being adjusted each quarter by its own performance,interest rate and global stock market movements of previous quarter.
Directory of Open Access Journals (Sweden)
Sasipa Pojanavatee
2014-12-01
Full Text Available The existing literature finds conflicting results on the magnitude of price linkages between equity mutual funds and the stock market. The study contends that in an optimal lagged model, the expectations of future prices using knowledge of past price behaviour in a particular equity mutual fund category will improve forecasts of prices of other equity mutual fund categories and the stock market index. The evidence shows that the long-run pricing of equity mutual funds is cointegrated with the stock market index. In the short run, the results indicate that some equity mutual fund categories possess both long-run and short-run exogeneity with the stock market. Therefore, the short-run dynamic indicates short-run Granger causal links running between different equity mutual fund categories.
Electricity consumption and economic growth in Burkina Faso: A cointegration analysis
International Nuclear Information System (INIS)
Ouedraogo, Idrissa M.
2010-01-01
This study empirically establishes the direction of causality between electricity consumption and economic growth in Burkina Faso for the period 1968-2003. The bounds test yields evidence of cointegration between electricity consumption, GDP, and capital formation when electricity consumption and GDP are used as dependent variable. Causality results indicate that there is no significant causal relationship between electricity consumption and investment. Estimates, however, detect in the long-run a bidirectional causal relationship between electricity use and real GDP. There is also evidence of a positive feedback causal relationship between GDP and capital formation. Burkina Faso is therefore an energy dependent country. It is also a country in which electricity consumption is growing with the level of income. All of this shows that electricity is a significant factor in socio-economic development in Burkina Faso; as such, energy policy must be implemented to ensure that electricity generates fewer potential negative impacts.
The role of remittances in the stability of money demand in Pakistan: A cointegration analysis
Directory of Open Access Journals (Sweden)
Ghumro Niaz Hussain
2017-01-01
Full Text Available The paper examines the dynamic relationship between the series of monetary aggregates M1 and M2 for the period 1972-2014. M1 and M2 are the dependent variables, while the explanatory variables are real income, discount rate, inflation rate, real exchange rate, and remittances. The ARDL bounds testing approach to cointegration is used to investigate the existence of long-run and short-run effects of remittances on monetary aggregates. The results show that remittances exert only positive effects on real narrow money demand in the end, suggesting that in Pakistan remittances are used for the purpose of consumption. Both money demand functions are stable in Pakistan, but the longrun effect of M1 remittances is a faster speed of adjustment to equilibrium (26.2% than M2 remittances (21.3%. It is recommended that M1 be used as a monetary tool in Pakistan.
Energy Technology Data Exchange (ETDEWEB)
Sa' ad, Suleiman [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, Surrey GU2 7XH (United Kingdom)
2009-11-15
This paper uses selection criteria from various models in a bounds testing approach to cointegration to estimate the price and income elasticities of demand for total petroleum products (gasoline and diesel) and gasoline share in total products in Indonesia. The results suggest that both total products and gasoline share estimates are more responsive to changes in income than changes in the real price of petroleum products. These results have important policy implications as they suggest that policy makers may need to use market-based pricing policies and other policies such as public enlightenment in addition to regulations like minimum energy efficiency standards to promote efficiency and conservation and curb the rising consumption of petroleum products in Indonesia. (author)
International Nuclear Information System (INIS)
Sa'ad, Suleiman
2009-01-01
This paper uses selection criteria from various models in a bounds testing approach to cointegration to estimate the price and income elasticities of demand for total petroleum products (gasoline and diesel) and gasoline share in total products in Indonesia. The results suggest that both total products and gasoline share estimates are more responsive to changes in income than changes in the real price of petroleum products. These results have important policy implications as they suggest that policy makers may need to use market-based pricing policies and other policies such as public enlightenment in addition to regulations like minimum energy efficiency standards to promote efficiency and conservation and curb the rising consumption of petroleum products in Indonesia. (author)
International Nuclear Information System (INIS)
Ramanathan, R.
1999-01-01
In developing countries like India, consumption of petroleum products has implications on its balance of payments, economic growth and fiscal deficit. Gasoline is one of the prime petroleum products. In this paper, the relationship between gasoline demand, national income and price of gasoline is empirically examined using cointegration and error correction techniques. The time frame of the analysis is from 1972-1973 to 1993-1994. It has been found that gasoline demand is likely to increase significantly for a given increase in the gross domestic product. The increase will be larger in the long-run (2.682) than in the short-run (1.178). Gasoline demand is relatively inelastic to price changes, both in the long and short terms. The error correction model has shown that gasoline demand adjusts to their respective long-run equilibrium at a relatively slow rate, with about 28% of adjustment taking place in the first year. 23 refs
Directory of Open Access Journals (Sweden)
Juan Carlos Muñoz
2008-12-01
Full Text Available The Environmental Kuznets Curve hypothesis suggests the existence of an inverted U-shaped relationship between environmental degradation and income. Several economists assume that the environmental impacts occurred during the first stages of the development process will be reverted as a result of economic growth. Yet Perman and Stern (2003 have argued that the econometric methods used in the earlier analysis of the EKC are inappropriate, given the time properties of the series. This article examines the appropriateness of the EKC for a panel of 46 countries and 21 periods by implementing individual and panel tests for unit roots and cointegration. An error correction model is also estimated. The results do not support evidence of a common EKC for the countries analyzed.
Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.
2018-03-01
We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
Directory of Open Access Journals (Sweden)
Jinchao Feng
2018-03-01
Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.
International Nuclear Information System (INIS)
Amusa, Hammed; Amusa, Kafayat; Mabugu, Ramos
2009-01-01
Electricity demand in South Africa has grown at a very rapid rate over the past decade. As part of reform initiatives to enhance long-term sustainability of the country's electricity industry, South Africa's authorities have in recent years sought to develop an electricity pricing framework that is cost reflective and forms the cornerstone of demand management schemes meant to foster changes in consumption behaviour and enhance efficiency in resource use. The effects of any pricing policy on aggregate electricity consumption will depend on a useful understanding of the factors that influence electricity demand, and the magnitude to which electricity demand responds to changes in such factors. In this context, this paper applies the bounds testing approach to cointegration within an autoregressive distributed lag framework to examine the aggregate demand for electricity in South Africa during the period 1960-2007. The results indicate that in the long run, income is the main determinant of electricity demand. With electricity prices having an insignificant effect on aggregate electricity demand, future pricing policies will need to ensure that electricity prices are cost reflective and enhance efficiency of electricity supply and use.
Energy Technology Data Exchange (ETDEWEB)
Amusa, Hammed; Mabugu, Ramos [Financial and Fiscal Commission, Private Bag X69, Halfway Gardens, 1685 Midrand (South Africa); Amusa, Kafayat [Department of Economics, University of South Africa, P.O. Box 392, UNISA 0003 (South Africa)
2009-10-15
Electricity demand in South Africa has grown at a very rapid rate over the past decade. As part of reform initiatives to enhance long-term sustainability of the country's electricity industry, South Africa's authorities have in recent years sought to develop an electricity pricing framework that is cost reflective and forms the cornerstone of demand management schemes meant to foster changes in consumption behaviour and enhance efficiency in resource use. The effects of any pricing policy on aggregate electricity consumption will depend on a useful understanding of the factors that influence electricity demand, and the magnitude to which electricity demand responds to changes in such factors. In this context, this paper applies the bounds testing approach to cointegration within an autoregressive distributed lag framework to examine the aggregate demand for electricity in South Africa during the period 1960-2007. The results indicate that in the long run, income is the main determinant of electricity demand. With electricity prices having an insignificant effect on aggregate electricity demand, future pricing policies will need to ensure that electricity prices are cost reflective and enhance efficiency of electricity supply and use. (author)
Modelling the rand and commodity prices: A Granger causality and cointegration analysis
Directory of Open Access Journals (Sweden)
Xolani Ndlovu
2014-11-01
Full Text Available This paper examines the ‘commodity currency’ hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.
Impact of Foreign Direct Investment and Economic Growth in Ghana: A Cointegration Analysis
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Samuel Antwi
2013-07-01
Full Text Available Foreign direct investment (FDI has been an important source of economic growth for Ghana, bringing in capital investment, technology and management knowledge needed for economic growth. This paper aims to study the relationship between FDI and economic growth in Ghana for the period 1980-2010 using time series data. The data used in this study was mainly secondary data collected from the period, 1980 to 2010 consisting of yearly observations for each variable. The real GDP growth and foreign direct investment net inflows as percent of GDP (FDI ratio data were taken from the World Banks World Development Indicators 2011 CD Rom. Yearly time series data covering the period 1980-2010 for which data was available was used. The cointegration methodology is applied on yearly data of FDI, GDP and GNI to determine the extent to which these variables are related. The study establishes that a long-run equilibrium and causal relationship exists between the dependent variable; FDI and the two independent variables under consideration namely, GDP and GNI. It was determined that in the short-run, effects of GDP and GNI volatility on FDI are nearly imaginary. These findings hold practical implications for policy makers, government and investors.
Lu, Wen-Cheng
2017-11-01
This article aims to investigate the relationship among renewable energy consumption, carbon dioxide (CO 2 ) emissions, and GDP using panel data for 24 Asian countries between 1990 and 2012. Panel cross-sectional dependence tests and unit root test, which considers cross-sectional dependence across countries, are used to ensure that the empirical results are correct. Using the panel cointegration model, the vector error correction model, and the Granger causality test, this paper finds that a long-run equilibrium exists among renewable energy consumption, carbon emission, and GDP. CO 2 emissions have a positive effect on renewable energy consumption in the Philippines, Pakistan, China, Iraq, Yemen, and Saudi Arabia. A 1% increase in GDP will increase renewable energy by 0.64%. Renewable energy is significantly determined by GDP in India, Sri Lanka, the Philippines, Thailand, Turkey, Malaysia, Jordan, United Arab Emirates, Saudi Arabia, and Mongolia. A unidirectional causality runs from GDP to CO 2 emissions, and two bidirectional causal relationships were found between CO 2 emissions and renewable energy consumption and between renewable energy consumption and GDP. The findings can assist governments in curbing pollution from air pollutants, execute energy conservation policy, and reduce unnecessary wastage of energy.
Colombian equity return and narrow money supply: an asymmetric cointegration analysis
Directory of Open Access Journals (Sweden)
Chu V. Nguyen
2012-12-01
Full Text Available The asymmetric, cointegrating relationship between the return on equity market and the narrowly defined money supply is documented. In fact, equity return and the monthly percentage change in the Colombian money supply M1 spread adjusts to the threshold value slower when a contractionary countercyclical policy action or an economic shock causes the money supply M1 to fall relative to the share price index, widening their spread, than when an expansionary countercyclical monetary policy action or a shock causes money supply M1 to move in the opposite direction, narrowing their spread. The empirical findings further indicate the impact lag on the Colombian monetary policy in the equity market is two years. These empirical findings should be of interest to both domestic and international investors who are interested in the Colombian equity market. The results also reveal the presence of both the neoclassical and the post-Keynesian positions on the relationship between equity return and money supply M1 in the Colombian financial market. In the age of globalization, these findings may provide a better understanding of the impact of the countercyclical monetary policy on the equity market in Latin American economies.
International Nuclear Information System (INIS)
Solarin, Sakiru Adebola; Shahbaz, Muhammad
2013-01-01
This paper investigates the causal relationship between economic growth, urbanisation and electricity consumption in the case of Angola, while utilizing the data over the period of 1971–2009. We have applied Lee and Strazicich (2003. The Review of Economics and Statistics 63, 1082–1089; 2004. Working Paper. Department of Economics, Appalachian State University) unit root tests to examine the stationarity properties of the series. Using the Gregory–Hansen structural break cointegration procedure as a complement, we employ the ARDL bounds test to investigate long run relationships. The VECM Granger causality test is subsequently used to examine the direction of causality between economic growth, urbanisation, and electricity consumption. Our results indicate the existence of long run relationships. We further observe evidence in favour of bidirectional causality between electricity consumption and economic growth. The feedback hypothesis is also found between urbanisation and economic growth. Urbanisation and electricity consumption Granger cause each other. We conclude that Angola is energy-dependent country. Consequently, the relevant authorities should boost electricity production as one of the means of achieving sustainable economic development in the long run. - Highlights: • We consider the link between electricity consumption and economic growth in Angola. • Urbanisation is added to turn the research into a trivariate investigation. • Various time series procedures are used. • Results show that increasing electricity will improve economic growth in Angola. • Results show urbanisations reduced economic growth during civil war
Non-parametric production analysis of pesticides use in the Netherlands
Oude Lansink, A.G.J.M.; Silva, E.
2004-01-01
Many previous empirical studies on the productivity of pesticides suggest that pesticides are under-utilized in agriculture despite the general held believe that these inputs are substantially over-utilized. This paper uses data envelopment analysis (DEA) to calculate non-parametric measures of the
Mittag, Kathleen Cage
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Data analysis with small samples and non-normal data nonparametrics and other strategies
Siebert, Carl F
2017-01-01
Written in everyday language for non-statisticians, this book provides all the information needed to successfully conduct nonparametric analyses. This ideal reference book provides step-by-step instructions to lead the reader through each analysis, screenshots of the software and output, and case scenarios to illustrate of all the analytic techniques.
Nonparametric inference in nonlinear principal components analysis : exploration and beyond
Linting, Mariëlle
2007-01-01
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),
Factors influencing CO2 emissions in China's power industry: Co-integration analysis
International Nuclear Information System (INIS)
Zhao, Xiaoli; Ma, Qian; Yang, Rui
2013-01-01
More than 40% of China's total CO 2 emissions originate from the power industry. The realization of energy saving and emission reduction within China's power industry is therefore crucial in order to achieve CO 2 emissions reduction in this country. This paper applies the autoregressive-distributed lag (ARDL) co-integration model to study the major factors which have influenced CO 2 emissions within China's power industry from 1980 to 2010. Results have shown that CO 2 emissions from China's power industry have been increasing rapidly. From 1980 to 2010, the average annual growth rate was 8.5%, and the average growth rate since 2002 has amounted to 10.5%. Secondly, the equipment utilization hour (as an indicator of the power demand) has the greatest influence on CO 2 emissions within China's power industry. In addition, the impact of the industrial added value of the power sector on CO 2 emissions is also positive from a short-term perspective. Thirdly, the Granger causality results imply that one of the important motivators behind China's technological progress, within the power industry, originates from the pressures created by a desire for CO 2 emissions reduction. Finally, this paper provides policy recommendations for energy saving and emission reduction for China's power industry. - Highlights: ► We study the major factors influencing China's power industry CO 2 emissions. ► The average annual growth rate of CO 2 emission from power industry is calculated. ► Installed capacity has the greatest influence on power industry CO 2 emission. ► The Granger causality between CO 2 emission and its effecting factors is analyzed
Directory of Open Access Journals (Sweden)
Jinchao Li
2018-01-01
Full Text Available Electric grid investment demand analysis is significant to reasonably arranging construction funds for the electric grid and reduce costs. This paper used the panel data of electric grid investment from 23 provinces of China between 2004 and 2016 as samples to analyze the influence between electric grid investment demand and GDP, population scale, social electricity consumption, installed electrical capacity, and peak load based on co-integration tests. We find that GDP and peak load have positive influences on electric grid investment demand, but the impact of population scale, social electricity consumption, and installed electrical capacity on electric grid investment is not remarkable. We divide different regions in China into the eastern region, central region, and western region to analyze influence factors of electric grid investment, finally obtaining key factors in the eastern, central, and western regions. In the end, according to the analysis of key factors, we make a prediction about China’s electric grid investment for 2020 in different scenarios. The results offer a certain understanding for the development trend of China’s electric grid investment and contribute to the future development of electric grid investment.
Multilevel Latent Class Analysis: Parametric and Nonparametric Models
Finch, W. Holmes; French, Brian F.
2014-01-01
Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
Wang, Wenshuo; Xi, Junqiang; Zhao, Ding
2017-01-01
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number...
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
DEFF Research Database (Denmark)
Tan, Qihua; Thomassen, Mads; Burton, Mark
2017-01-01
the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
Richardson, Amy; Hudgens, Michael G.; Gilbert, Peter B.; Fine, Jason P.
2015-01-01
This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed. PMID:25663743
Bornkamp, Björn; Ickstadt, Katja
2009-03-01
In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.
FBST for Cointegration Problems
Diniz, M.; Pereira, C. A. B.; Stern, J. M.
2008-11-01
In order to estimate causal relations, the time series econometrics has to be aware of spurious correlation, a problem first mentioned by Yule [21]. To solve the problem, one can work with differenced series or use multivariate models like VAR or VEC models. In this case, the analysed series are going to present a long run relation i.e. a cointegration relation. Even though the Bayesian literature about inference on VAR/VEC models is quite advanced, Bauwens et al. [2] highlight that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results." This paper presents the Full Bayesian Significance Test applied to cointegration rank selection tests in multivariate (VAR/VEC) time series models and shows how to implement it using available in the literature and simulated data sets. A standard non-informative prior is assumed.
Malthus in Cointegration Space
DEFF Research Database (Denmark)
Møller, Niels Framroze; Sharp, Paul Richard
We analyze Malthus' (1798) model when labor demand shifts persistently. The Malthusian ideas are formalized and derived in terms of stationarity and cointegration, and the implied restrictions are tested against English pre-industrial data 1560-1760. The evidence suggests a negligible marginal...... productivity effect of population on real income, implying that the Malthusian "check" relations should be analyzed as cointegrating relations. The data support highly significant preventive checks working via marriages, but weak (in-significant) positive checks. These results are remarkably clear-cut. We...
International Nuclear Information System (INIS)
Tuerkekul, Berna; Unakitan, Goekhan
2011-01-01
Agriculture has an important role in every country's development. Particularly, the contribution of agriculture to development and competitiveness is increasing with agricultural productivity growth. Productivity, in turn, is closely associated with direct and indirect use of energy as an input. Therefore, the importance of energy in agriculture cannot be denied as one of the basic inputs to the economic growth process. Following the importance of energy in Turkish agriculture, this study aims to estimate the long- and short-run relationship of energy consumption, agricultural GDP, and energy prices via co-integration and error correction (ECM) analysis. Annual data from 1970 to 2008 for diesel and electricity consumptions are utilized to estimate long-run and short-run elasticities. According to ECM analysis, for the diesel demand model, the long-run income and price elasticities were calculated as 1.47 and -0.38, respectively. For the electricity demand model, income and price elasticities were calculated at 0.19 and -0.72, respectively, in the long run. Briefly, in Turkey, support for energy use in agriculture should be continued in order to ensure sustainability in agriculture, increase competitiveness in international markets, and balance farmers' income. - Research highlights: → We estimate the long and short run elasticities for diesel and electricity demands in agriculture. → The long-run income and price elasticities calculated as 1.47 and 0.38, respectively for diesel. → The long run Income and price elasticities calculated as 0.19 and 0.72 for electricity.
Energy Technology Data Exchange (ETDEWEB)
Tuerkekul, Berna, E-mail: berna.turkekul@ege.edu.t [Department of Agricultural Economics, Faculty of Agriculture, Ege University, 35100 Izmir (Turkey); Unakitan, Goekhan, E-mail: unakitan@nku.edu.t [Department of Agricultural Economics, Faculty of Agriculture, Namik Kemal University, Tekirdag (Turkey)
2011-05-15
Agriculture has an important role in every country's development. Particularly, the contribution of agriculture to development and competitiveness is increasing with agricultural productivity growth. Productivity, in turn, is closely associated with direct and indirect use of energy as an input. Therefore, the importance of energy in agriculture cannot be denied as one of the basic inputs to the economic growth process. Following the importance of energy in Turkish agriculture, this study aims to estimate the long- and short-run relationship of energy consumption, agricultural GDP, and energy prices via co-integration and error correction (ECM) analysis. Annual data from 1970 to 2008 for diesel and electricity consumptions are utilized to estimate long-run and short-run elasticities. According to ECM analysis, for the diesel demand model, the long-run income and price elasticities were calculated as 1.47 and -0.38, respectively. For the electricity demand model, income and price elasticities were calculated at 0.19 and -0.72, respectively, in the long run. Briefly, in Turkey, support for energy use in agriculture should be continued in order to ensure sustainability in agriculture, increase competitiveness in international markets, and balance farmers' income. - Research highlights: {yields} We estimate the long and short run elasticities for diesel and electricity demands in agriculture. {yields} The long-run income and price elasticities calculated as 1.47 and 0.38, respectively for diesel. {yields} The long run Income and price elasticities calculated as 0.19 and 0.72 for electricity.
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.
Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben
2017-06-06
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
Likelihood based testing for no fractional cointegration
DEFF Research Database (Denmark)
Lasak, Katarzyna
. The standard cointegration analysis only considers the assumption that deviations from equilibrium can be integrated of order zero, which is very restrictive in many cases and may imply an important loss of power in the fractional case. We consider the alternative hypotheses with equilibrium deviations...... that can be mean reverting with order of integration possibly greater than zero. Moreover, the degree of fractional cointegration is not assumed to be known, and the asymptotic null distribution of both tests is found when considering an interval of possible values. The power of the proposed tests under...
Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines
Directory of Open Access Journals (Sweden)
Konrad Zolna
2015-01-01
Full Text Available Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.
Curceac, S.; Ternynck, C.; Ouarda, T.
2015-12-01
Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed
KHATTAB, Ahmed; IHADIYAN, Abid
2015-01-01
Abstract. This article aims at examining the impact of financial liberalization on the economic growth in the North African countries. The econometric study, which covers the period between 1995 and 2013, relies on a sample composed of four Northern African countries and referring to the database of the World Bank data (2013), Heritage Foundation (2013) and Financial Openness of (the Institute for international and development Economics, 2009). The estimate model of cointegration panel reveal...
Nonparametric bootstrap analysis with applications to demographic effects in demand functions.
Gozalo, P L
1997-12-01
"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt
International Nuclear Information System (INIS)
Peterson, James R.; Haas, Timothy C.; Lee, Danny C.
2000-01-01
Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network
International Nuclear Information System (INIS)
Fang, Zheng; Chang, Youngho
2016-01-01
This paper examines the cointegration and causal relationship between energy consumption and economic development in 16 Asia Pacific countries over the period 1970–2011 using the augmented production function which considers not only physical capital and labor but also human capital. This is likely among the first of the energy–growth nexus literature to include human capital in the multivariate framework. Using recently developed panel unit root test and cointegration test that allow for cross-sectional dependence, this paper finds a long-run cointegrating relationship between these variables. Continuously-updated fully modified (Cup-FM) estimates are subsequently compared with panel heterogeneous fully modified ordinary least squares (FMOLS) results to confirm the importance of accounting for interdependence across countries. The bootstrap panel Granger causality test results find economic growth Granger cause energy use in the region but the relationship varies for individual countries. - Highlights: • We study the causal link between energy and growth in 16 AP countries for 1970–2011. • Human capital is for the first time incorporated into the multivariate framework. • Recent panel methods allowing for cross sectional dependence is used. • Bootstrap panel Granger causality test results find GDP Granger causing energy use in the region. • The energy–growth relationship varies for individual countries.
Trend Analysis of Pahang River Using Non-Parametric Analysis: Mann Kendalls Trend Test
International Nuclear Information System (INIS)
Nur Hishaam Sulaiman; Mohd Khairul Amri Kamarudin; Mohd Khairul Amri Kamarudin; Ahmad Dasuki Mustafa; Muhammad Azizi Amran; Fazureen Azaman; Ismail Zainal Abidin; Norsyuhada Hairoma
2015-01-01
Flood is common in Pahang especially during northeast monsoon season from November to February. Three river cross station: Lubuk Paku, Sg. Yap and Temerloh were selected as area of this study. The stream flow and water level data were gathered from DID record. Data set for this study were analysed by using non-parametric analysis, Mann-Kendall Trend Test. The results that obtained from stream flow and water level analysis indicate that there are positively significant trend for Lubuk Paku (0.001) and Sg. Yap (<0.0001) from 1972-2011 with the p-value < 0.05. Temerloh (0.178) data from 1963-2011 recorded no trend for stream flow parameter but negative trend for water level parameter. Hydrological pattern and trend are extremely affected by outside factors such as north east monsoon season that occurred in South China Sea and affected Pahang during November to March. There are other factors such as development and management of the areas which can be considered as factors affected the data and results. Hydrological Pattern is important to indicate the river trend such as stream flow and water level. It can be used as flood mitigation by local authorities. (author)
International Nuclear Information System (INIS)
McIntee, Erin; Viglino, Emilie; Rinke, Caitlin; Kumor, Stephanie; Ni Liqiang; Sigman, Michael E.
2010-01-01
Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot 'drill down' spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.
Directory of Open Access Journals (Sweden)
Stylianou Tasos
2012-06-01
Full Text Available This paper investigates the nexus between economic growth and defense spending for three adjacent countries, namely Greece, Turkey and Cyprus. Greece and Cyprus, members-countries of European Union spend much more money than other member countries of EU relatively to their GDP. Turkey is in accession negotiations with EU and is among the top 15 countries with the highest military expenditure. These three countries are particularly interesting case studies because of their high military burdens and the bad relations between them (Greece and Cyprus opposite Turkey. The empirical analysis is based on panel data analysis of data over the period 1960 – 2006.
United Kingdom and United States Tourism Demand for Malaysia:A Cointegration Analysis
Habibi, Fateh; Abdul Rahim, Khalid; Chin, Lee
2008-01-01
Tourism industry has been an important contributor to the Malaysia economy. In this paper we inspect variations in the long run demand for tourism from United Kingdom and United States to Malaysia. The demand for tourism has been explained by macroeconomic variables, including income in origin countries, tourism prices in Malaysia, and travel cost between the two countries. Annual data from 1972 to 2006 are used for the analysis. Augmented Dickey-Fuller and Johansen’s maximum likelihood tests...
Environment Kuznets curve for CO{sub 2} emissions. A cointegration analysis for China
Energy Technology Data Exchange (ETDEWEB)
Jalil, Abdul [Centre for Economic Development Research, Wuhan University, Wuhan 437002 (China); Mahmud, Syed F. [Department of Economics, Bilkent University, Ankara 06800 (Turkey)
2009-12-15
This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO{sub 2} emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO{sub 2} emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO{sub 2} emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO{sub 2} emissions. (author)
Environment Kuznets curve for CO{sub 2} emissions: A cointegration analysis for China
Energy Technology Data Exchange (ETDEWEB)
Jalil, Abdul, E-mail: jalil.hanif@gmail.co [Centre for Economic Development Research, Wuhan University, Wuhan 437002 (China); Mahmud, Syed F., E-mail: syed@bilkent.edu.t [Department of Economics, Bilkent University, Ankara 06800 (Turkey)
2009-12-15
This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO{sub 2} emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO{sub 2} emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO{sub 2} emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO{sub 2} emissions.
Environment Kuznets curve for CO2 emissions: A cointegration analysis for China
International Nuclear Information System (INIS)
Jalil, Abdul; Mahmud, Syed F.
2009-01-01
This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO 2 emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO 2 emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO 2 emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO 2 emissions.
International Nuclear Information System (INIS)
Saboori, Behnaz; Sulaiman, Jamalludin; Mohd, Saidatulakmal
2012-01-01
This paper attempts to establish a long-run as well as causal relationship between economic growth and carbon dioxide (CO 2 ) emissions for Malaysia. Using data for the years from 1980 to 2009, the Environmental Kuznets Curve (EKC) hypothesis was tested utilizing the Auto Regressive Distributed Lag (ARDL) methodology. The empirical results suggest the existence of a long-run relationship between per capita CO 2 emissions and real per capita Gross Domestic Product (GDP) when the CO 2 emissions level is the dependent variable. We found an inverted-U shape relationship between CO 2 emissions and GDP in both short and long-run, thus supporting the EKC hypothesis. The Granger Causality test based on the Vector Error Correction Model (VECM) presents an absence of causality between CO 2 emissions and economic growth in the short-run while demonstrating uni-directional causality from economic growth to CO 2 emissions in the long-run. - Highlights: ► We tested the dynamic relationship between economic growth and CO 2 emissions. ► The Environmental Kuznets Curve hypothesis was tested using bounds testing approach. ► The empirical analysis confirms the existence of EKC for Malaysia. ► Causality results in an absence of causality between CO 2 and income in the short-run. ► There is uni-directional causality from income to CO 2 emissions in the long-run.
Directory of Open Access Journals (Sweden)
Andreea-Cristina PETRICA
2016-12-01
Full Text Available The goal of this paper is to investigate cointegration between Bucharest Stock Exchange and three European Stock Markets: Germany,France and Poland, respectively. The choice of the European markets is based on the net assets investment of the Romanian mandatory private pension funds. On June 30, 2016, according to the Romanian Financial Supervisory Authority 91.28% of all investments in shares of private pension funds have been performed in Romanian shares, while the rest of 8.72% (0.44 billion lei have been performed in shares issued by Germany (2.34%, France (2.23%, Poland (0.89% and other countries. Having the intention of achieving maximizing returns by managing risk, and also to capture the co-movements in the above markets,we perform the cointegration analysis to examine portfolio diversification of the Romanian mandatory private pension funds. The empirical analysis is based on daily closing prices of the BET Index, DAX 30 Index, CAC 40 Index and WIG 20 Index and covers the period from 30 January 2006 to 27 September 2016 (2713 observations.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.
Ghosh, Debashis; Chinnaiyan, Arul M
2009-01-01
In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.
Energy Technology Data Exchange (ETDEWEB)
Takamizawa, Hisashi, E-mail: takamizawa.hisashi@jaea.go.jp; Itoh, Hiroto, E-mail: ito.hiroto@jaea.go.jp; Nishiyama, Yutaka, E-mail: nishiyama.yutaka93@jaea.go.jp
2016-10-15
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Directory of Open Access Journals (Sweden)
Ayad Hicham
2017-06-01
Full Text Available This paper attempts the dynamic causal relationship between poverty reduction measured as consumption per capita and financial development measured as Kaopen and Milesi-Ferreti proxies, trade openness measured measured by the sum of total exports and total imports as a percentage of GDP at 2005 constant prices and economic growth as measured by GDP per capita for 14 selected Arabic countries within the panel co-integration techniques and TYDL Granger causality approach (1996, the results show that the poor people in Arabic countries (the selected countries did not benefit from liberalization systems and economic growth for the period because both of finance-led poverty and trade-led poverty seem to be rejected. The study, therefore, recommends that policy makers should stop the financial braking and adopt new financial policies that allow reducing poverty rates.
Oscillating systems with cointegrated phase processes
DEFF Research Database (Denmark)
Østergaard, Jacob; Rahbek, Anders; Ditlevsen, Susanne
2017-01-01
We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network...... that resembles biological processes. In particular we study a network of Winfree oscillators, for which we present a statistical analysis of various simulated networks, where we conclude on the coupling structure: the direction of feedback in the phase processes and proportional coupling strength between...... individual components of the system. We show that we can correctly classify the network structure for such a system by cointegration analysis, for various types of coupling, including uni-/bi-directional and all-to-all coupling. Finally, we analyze a set of EEG recordings and discuss the current...
Performances of non-parametric statistics in sensitivity analysis and parameter ranking
International Nuclear Information System (INIS)
Saltelli, A.
1987-01-01
Twelve parametric and non-parametric sensitivity analysis techniques are compared in the case of non-linear model responses. The test models used are taken from the long-term risk analysis for the disposal of high level radioactive waste in a geological formation. They describe the transport of radionuclides through a set of engineered and natural barriers from the repository to the biosphere and to man. The output data from these models are the dose rates affecting the maximum exposed individual of a critical group at a given point in time. All the techniques are applied to the output from the same Monte Carlo simulations, where a modified version of Latin Hypercube method is used for the sample selection. Hypothesis testing is systematically applied to quantify the degree of confidence in the results given by the various sensitivity estimators. The estimators are ranked according to their robustness and stability, on the basis of two test cases. The conclusions are that no estimator can be considered the best from all points of view and recommend the use of more than just one estimator in sensitivity analysis
International Nuclear Information System (INIS)
Bekiros, Stelios D.; Diks, Cees G.H.
2008-01-01
The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods October 1991-October 1999 and November 1999-October 2007, with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetric GARCH effects and/or statistically significant higher order conditional moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time. (author)
2015-12-01
The material flow account of Tangshan City was established by material flow analysis (MFA) method to analyze the periodical characteristics of material input and output in the operation of economy-environment system, and the impact of material input and output intensities on economic development. Using econometric model, the long-term interaction mechanism and relationship among the indexes of gross domestic product (GDP) , direct material input (DMI), domestic processed output (DPO) were investigated after unit root hypothesis test, Johansen cointegration test, vector error correction model, impulse response function and variance decomposition. The results showed that during 1992-2011, DMI and DPO both increased, and the growth rate of DMI was higher than that of DPO. The input intensity of DMI increased, while the intensity of DPO fell in volatility. Long-term stable cointegration relationship existed between GDP, DMI and DPO. Their interaction relationship showed a trend from fluctuation to gradual ste adiness. DMI and DPO had strong, positive impacts on economic development in short-term, but the economy-environment system gradually weakened these effects by short-term dynamically adjusting indicators inside and outside of the system. Ultimately, the system showed a long-term equilibrium relationship. The effect of economic scale on economy was gradually increasing. After decomposing the contribution of each index to GDP, it was found that DMI's contribution grew, GDP's contribution declined, DPO's contribution changed little. On the whole, the economic development of Tangshan City has followed the traditional production path of resource-based city, mostly depending on the material input which caused high energy consumption and serous environmental pollution.
Single molecule force spectroscopy at high data acquisition: A Bayesian nonparametric analysis
Sgouralis, Ioannis; Whitmore, Miles; Lapidus, Lisa; Comstock, Matthew J.; Pressé, Steve
2018-03-01
Bayesian nonparametrics (BNPs) are poised to have a deep impact in the analysis of single molecule data as they provide posterior probabilities over entire models consistent with the supplied data, not just model parameters of one preferred model. Thus they provide an elegant and rigorous solution to the difficult problem encountered when selecting an appropriate candidate model. Nevertheless, BNPs' flexibility to learn models and their associated parameters from experimental data is a double-edged sword. Most importantly, BNPs are prone to increasing the complexity of the estimated models due to artifactual features present in time traces. Thus, because of experimental challenges unique to single molecule methods, naive application of available BNP tools is not possible. Here we consider traces with time correlations and, as a specific example, we deal with force spectroscopy traces collected at high acquisition rates. While high acquisition rates are required in order to capture dwells in short-lived molecular states, in this setup, a slow response of the optical trap instrumentation (i.e., trapped beads, ambient fluid, and tethering handles) distorts the molecular signals introducing time correlations into the data that may be misinterpreted as true states by naive BNPs. Our adaptation of BNP tools explicitly takes into consideration these response dynamics, in addition to drift and noise, and makes unsupervised time series analysis of correlated single molecule force spectroscopy measurements possible, even at acquisition rates similar to or below the trap's response times.
Nonparametric Bayesian inference for mean residual life functions in survival analysis.
Poynor, Valerie; Kottas, Athanasios
2018-01-19
Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel
2017-10-01
This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.
Koçak, Emrah; Şarkgüneşi, Aykut
2018-01-01
Pollution haven hypothesis (PHH), which is defined as foreign direct investment inducing a raising impact on the pollution level in the hosting country, is lately a subject of discussion in the field of economics. This study, within the scope of related discussion, aims to look into the potential impact of foreign direct investments on CO 2 emission in Turkey in 1974-2013 period using environmental Kuznets curve (EKC) model. For this purpose, Maki (Econ Model 29(5):2011-2015, 2012) structural break cointegration test, Stock and Watson (Econometrica 61:783-820, 1993) dynamic ordinary least square estimator (DOLS), and Hacker and Hatemi-J (J Econ Stud 39(2):144-160, 2012) bootstrap test for causality method are used. Research results indicate the existence of a long-term balance relationship between FDI, economic growth, energy usage, and CO 2 emission. As per this relationship, in Turkey, (1) the potential impact of FDI on CO 2 emission is positive. This result shows that PHH is valid in Turkey. (2) Moreover, this is not a one-way relationship; the changes in CO 2 emission also affect FDI entries. (3) The results also provide evidence for the existence of the EKC hypothesis in Turkey. Within the frame of related findings, the study concludes several polities and presents various suggestions.
Directory of Open Access Journals (Sweden)
Irena Raguz
2013-03-01
Full Text Available The aim of this paper is to investigate the existence and characteristics of both the long- and short-term relationships between FDI and the stock market in Croatia. The main hypothesis is that, in the long run, trends in FDI should determine the movement of the stock market through the channel of economic growth. However, in the short run, upward movement on the stock market positively affects Croatian FDI stock, as events on the stock market signalize the vitality and investment climate of the domestic market to foreign investors. The long-term connection is tested by two cointegration approaches; the results of both models suggest the absence of a long-term relationship among observed variables, which may be explained by the lack of connection between FDI and economic growth in Croatia. The short-run relationship is investigated by a two-variable VAR model, and the results obtained are consistent with the theoretical assumptions, as the stock market did prove to be an important short-term determinant of FDI in Croatia.
A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.
Fronczyk, Kassandra; Kottas, Athanasios
2014-03-01
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.
Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis
Hof, Stefanie
2014-01-01
Private tutoring has become popular throughout the world. However, evidence for the effect of private tutoring on students' academic outcome is inconclusive; therefore, this paper presents an alternative framework: a nonparametric bounds method. The present examination uses, for the first time, a large representative data-set in a European setting…
Non-Parametric Kinetic (NPK Analysis of Thermal Oxidation of Carbon Aerogels
Directory of Open Access Journals (Sweden)
Azadeh Seifi
2017-05-01
Full Text Available In recent years, much attention has been paid to aerogel materials (especially carbon aerogels due to their potential uses in energy-related applications, such as thermal energy storage and thermal protection systems. These open cell carbon-based porous materials (carbon aerogels can strongly react with oxygen at relatively low temperatures (~ 400°C. Therefore, it is necessary to evaluate the thermal performance of carbon aerogels in view of their energy-related applications at high temperatures and under thermal oxidation conditions. The objective of this paper is to study theoretically and experimentally the oxidation reaction kinetics of carbon aerogel using the non-parametric kinetic (NPK as a powerful method. For this purpose, a non-isothermal thermogravimetric analysis, at three different heating rates, was performed on three samples each with its specific pore structure, density and specific surface area. The most significant feature of this method, in comparison with the model-free isoconversional methods, is its ability to separate the functionality of the reaction rate with the degree of conversion and temperature by the direct use of thermogravimetric data. Using this method, it was observed that the Nomen-Sempere model could provide the best fit to the data, while the temperature dependence of the rate constant was best explained by a Vogel-Fulcher relationship, where the reference temperature was the onset temperature of oxidation. Moreover, it was found from the results of this work that the assumption of the Arrhenius relation for the temperature dependence of the rate constant led to over-estimation of the apparent activation energy (up to 160 kJ/mol that was considerably different from the values (up to 3.5 kJ/mol predicted by the Vogel-Fulcher relationship in isoconversional methods
International Nuclear Information System (INIS)
Janurová, Kateřina; Briš, Radim
2014-01-01
Medical survival right-censored data of about 850 patients are evaluated to analyze the uncertainty related to the risk of mortality on one hand and compare two basic surgery techniques in the context of risk of mortality on the other hand. Colorectal data come from patients who underwent colectomy in the University Hospital of Ostrava. Two basic surgery operating techniques are used for the colectomy: either traditional (open) or minimally invasive (laparoscopic). Basic question arising at the colectomy operation is, which type of operation to choose to guarantee longer overall survival time. Two non-parametric approaches have been used to quantify probability of mortality with uncertainties. In fact, complement of the probability to one, i.e. survival function with corresponding confidence levels is calculated and evaluated. First approach considers standard nonparametric estimators resulting from both the Kaplan–Meier estimator of survival function in connection with Greenwood's formula and the Nelson–Aalen estimator of cumulative hazard function including confidence interval for survival function as well. The second innovative approach, represented by Nonparametric Predictive Inference (NPI), uses lower and upper probabilities for quantifying uncertainty and provides a model of predictive survival function instead of the population survival function. The traditional log-rank test on one hand and the nonparametric predictive comparison of two groups of lifetime data on the other hand have been compared to evaluate risk of mortality in the context of mentioned surgery techniques. The size of the difference between two groups of lifetime data has been considered and analyzed as well. Both nonparametric approaches led to the same conclusion, that the minimally invasive operating technique guarantees the patient significantly longer survival time in comparison with the traditional operating technique
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...
International Nuclear Information System (INIS)
Khoshroo, Alireza; Mulwa, Richard; Emrouznejad, Ali; Arabi, Behrouz
2013-01-01
Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production. In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming. The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. - Highlights: • The focus of this paper is to identify excessive use of energy and optimize energy consumption in grape production. • We measure the efficiency as a function of labor/machinery/chemicals/farmyard manure/diesel-fuel/electricity/water. • Data were obtained from 41 grape
von Hirschhausen, Christian R.; Cullmann, Astrid
2005-01-01
Abstract This paper applies parametric and non-parametric and parametric tests to assess the efficiency of electricity distribution companies in Germany. We address traditional issues in electricity sector benchmarking, such as the role of scale effects and optimal utility size, as well as new evidence specific to the situation in Germany. We use labour, capital, and peak load capacity as inputs, and units sold and the number of customers as output. The data cover 307 (out of 553) ...
Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu
2017-12-01
Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.
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...
Energy Technology Data Exchange (ETDEWEB)
Silva, Bruno Goncalves da; Parente, Virginia [Universidade de Sao Paulo (IEE/USP), SP (Brazil). Inst. de Eletrotecnica e Energia], emails: brunosilva@usp.br, vparente@iee.usp.br
2010-07-01
There are many studies aimed at estimating the long-run relationship between GDP and energy. The main objective of this paper is to analyze this relationship for Brazil, taking as a starting point to other studies already done with cointegration methodology to other countries of the world, in order to determine how the Brazilian domestic product is related to energy consumption country.
Robust non-parametric one-sample tests for the analysis of recurrent events.
Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia
2010-12-30
One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population. Copyright © 2010 John Wiley & Sons, Ltd.
Regression Technique of Cointegrated Processes | Aboagye-Sarfo ...
African Journals Online (AJOL)
Many financial series or microeconomic data are serially correlated, nonstationary and are found to be integrated. Cointegration has become a major tool in the analysis of financial and microeconomic time series since its introduction and has changed the way econometric modelling is carried out. This paper deals with the ...
Directory of Open Access Journals (Sweden)
Francisco G. Carneiro
2003-01-01
Full Text Available This paper investigates the validity of the Fisher effect hypothesis that it is the interest rate which moves to adjust to the anticipated changes in the rate of inflation. The analysis is carried out with monthly data for the period 1980-97 for three countries with recent histories of chronic high inflation: Argentina, Brazil, and Mexico. A co-integration analysis has provided evidence of a stable long-run equilibrium relationship between nominal interest rates and the inflation rate for the cases of Argentina and Brazil only.
NParCov3: A SAS/IML Macro for Nonparametric Randomization-Based Analysis of Covariance
Directory of Open Access Journals (Sweden)
Richard C. Zink
2012-07-01
Full Text Available Analysis of covariance serves two important purposes in a randomized clinical trial. First, there is a reduction of variance for the treatment effect which provides more powerful statistical tests and more precise confidence intervals. Second, it provides estimates of the treatment effect which are adjusted for random imbalances of covariates between the treatment groups. The nonparametric analysis of covariance method of Koch, Tangen, Jung, and Amara (1998 defines a very general methodology using weighted least-squares to generate covariate-adjusted treatment effects with minimal assumptions. This methodology is general in its applicability to a variety of outcomes, whether continuous, binary, ordinal, incidence density or time-to-event. Further, its use has been illustrated in many clinical trial settings, such as multi-center, dose-response and non-inferiority trials.NParCov3 is a SAS/IML macro written to conduct the nonparametric randomization-based covariance analyses of Koch et al. (1998. The software can analyze a variety of outcomes and can account for stratification. Data from multiple clinical trials will be used for illustration.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2014-01-01
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
Karabatsos, George
2017-02-01
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected
DEFF Research Database (Denmark)
Johansen, Søren
There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference. Finally we analyse some data on annual mean temperature...... and sea level, by applying the cointegrated vector autoregressive model, which explicitly takes into account the nonstationarity of the variables....
DEFF Research Database (Denmark)
Johansen, Søren
There are simple well-known conditions for the validity of regression and correlation as statistical tools. We analyse by examples the effect of nonstationarity on inference using these methods and compare them to model based inference. Finally we analyse some data on annual mean temperature...... and sea level, by applying the cointegrated vector autoregressive model, which explicitly takes into account the nonstationarity of the variables....
Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G
2018-03-01
Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.
Freitas, Carlos J. Pereira; Silva, Patrícia Pereira da
2013-01-01
This paper addresses the impact of the CO2 opportunity cost on the wholesale electricity price in the context of the Iberian electricity market (MIBEL), namely on the Portuguese system, for the period corresponding to the Phase II of the European Union Emission Trading Scheme (EU ETS). In the econometric analysis a vector error correction model (VECM) is specified to estimate both long–run equilibrium relations and short–run interactions between the electricity price and the fuel (natural gas...
International Nuclear Information System (INIS)
Jardinier, E.
2013-01-01
The current will of reducing environment and human hazards has led the scientists to imagine new solutions for nuclear waste reprocessing. Miniaturized online chemical analysis of industrial processes has in particular an important role to play to reduce effluent volumes, response times and costs. In this context, we present the design, fabrication and characterization of an integrated spectrophotometric sensor on glass for chemical analysis of radioactive cations. The device is called a - nano-channel waveguide - and is fabricated by reactive ion etching and ion exchange on glass. It is made of two borosilicate glass wafers bonded together. The first one contains a strip core and the second one a (100 ±10) nm deep nano-channel and a slab core. It allows the propagation of a hybrid mode, optimizing the fluid/guide wave interaction on a large wavelength range. Spectrometric measurements of a neodymium nitrate in nitric acid (pH 2) followed by statistical treatment have led to a limit of detection in terms of absorption coefficient of (3.7 ± 0.9) * 10 -3 cm -1 for a device length of (3.70 ± 0.05) cm and fluid volume as low as (7 ± 3) nL. A structure allowing to increase the interaction length and therefore further decrease the detection limit has been proposed as an outlook of this work, and a preliminary study for use in a nuclear environment has been performed. (author) [fr
Yau, C; Papaspiliopoulos, O; Roberts, G O; Holmes, C
2011-01-01
We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.
Cointegration approach for temperature effect compensation in Lamb-wave-based damage detection
International Nuclear Information System (INIS)
Dao, Phong B; Staszewski, Wieslaw J
2013-01-01
Lamb waves are often used in smart structures with integrated, low-profile piezoceramic transducers for damage detection. However, it is well known that the method is prone to contamination from a variety of interference sources including environmental and operational conditions. The paper demonstrates how to remove the undesired temperature effect from Lamb wave data. The method is based on the concept of cointegration that is partially built on the analysis of the non-stationary behaviour of time series. Instead of directly using Lamb wave responses for damage detection, two approaches are proposed: (i) analysis of cointegrating residuals obtained from the cointegration process of Lamb wave responses, (ii) analysis of stationary characteristics of Lamb wave responses before and after cointegration. The method is tested on undamaged and damaged aluminium plates exposed to temperature variations. The experimental results show that the method can: isolate damage-sensitive features from temperature variations, detect the existence of damage and classify its severity. (paper)
Nonparametric statistics for social and behavioral sciences
Kraska-MIller, M
2013-01-01
Introduction to Research in Social and Behavioral SciencesBasic Principles of ResearchPlanning for ResearchTypes of Research Designs Sampling ProceduresValidity and Reliability of Measurement InstrumentsSteps of the Research Process Introduction to Nonparametric StatisticsData AnalysisOverview of Nonparametric Statistics and Parametric Statistics Overview of Parametric Statistics Overview of Nonparametric StatisticsImportance of Nonparametric MethodsMeasurement InstrumentsAnalysis of Data to Determine Association and Agreement Pearson Chi-Square Test of Association and IndependenceContingency
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural ...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
A Dynamic Model of U.S. Sugar-Related Markets: A Cointegrated Vector Autoregression Approach
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 ...
Directory of Open Access Journals (Sweden)
Marius-Corneliu Marinas
2007-07-01
Full Text Available This paper attempts to analyze the relationship between exports, investments and economic growth in Romania. For the search of this relationship I use a multivariate autoregressive VAR model. The results of cointegration analysis showed that there is one cointegrated vector among exports, investments and economic growth. Granger causality tests based on error correction models (ECM have indicated that investment and export influences the steady-state level of GDP.
A Gaussian IV estimator of cointegrating relations
DEFF Research Database (Denmark)
Bårdsen, Gunnar; Haldrup, Niels
2006-01-01
In static single equation cointegration regression modelsthe OLS estimator will have a non-standard distribution unless regressors arestrictly exogenous. In the literature a number of estimators have been suggestedto deal with this problem, especially by the use of semi-nonparametricestimators. T......In static single equation cointegration regression modelsthe OLS estimator will have a non-standard distribution unless regressors arestrictly exogenous. In the literature a number of estimators have been suggestedto deal with this problem, especially by the use of semi...... in cointegrating regressions. These instruments are almost idealand simulations show that the IV estimator using such instruments alleviatethe endogeneity problem extremely well in both finite and large samples....
Decision support using nonparametric statistics
Beatty, Warren
2018-01-01
This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides the reader with a set of tools that can be used to validly analyze all data, regardless of type. Through numerous examples and exercises, this book explains why nonparametric statistics will lead to better decisions and how they are used to reach a decision, with a wide array of business applications. Online resources include exercise data, spreadsheets, and solutions.
Approaches to nonlinear cointegration with a view towards applications in SHM
International Nuclear Information System (INIS)
Cross, E J; Worden, K
2011-01-01
One of the major problems confronting the application of Structural Health Monitoring (SHM) to real structures is that of divorcing the effect of environmental changes from those imposed by damage. A recent development in this area is the import of the technique of cointegration from the field of econometrics. While cointegration is a mature technology within economics, its development has been largely concerned with linear time-series analysis and this places a severe constraint on its application - particularly in the new context of SHM where damage can often make a given structure nonlinear. The objective of the current paper is to introduce two possible approaches to nonlinear cointegration: the first is an optimisation-based method; the second is a variation of the established Johansen procedure based on the use of an augmented basis. Finally, the ideas of nonlinear cointegration will be explored through application to real SHM data from the benchmark project on the Z24 Highway Bridge.
Approaches to nonlinear cointegration with a view towards applications in SHM
Cross, E. J.; Worden, K.
2011-07-01
One of the major problems confronting the application of Structural Health Monitoring (SHM) to real structures is that of divorcing the effect of environmental changes from those imposed by damage. A recent development in this area is the import of the technique of cointegration from the field of econometrics. While cointegration is a mature technology within economics, its development has been largely concerned with linear time-series analysis and this places a severe constraint on its application - particularly in the new context of SHM where damage can often make a given structure nonlinear. The objective of the current paper is to introduce two possible approaches to nonlinear cointegration: the first is an optimisation-based method; the second is a variation of the established Johansen procedure based on the use of an augmented basis. Finally, the ideas of nonlinear cointegration will be explored through application to real SHM data from the benchmark project on the Z24 Highway Bridge.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Directory of Open Access Journals (Sweden)
Cem KADILAR
2005-01-01
Full Text Available This study includes an econometric analysis of the export demand behaviour by using Turkey's annual data that cover 32 years periods from 1970 to 2002. In the study, the 'bounds test' method of Pesaran et al (2001 was used to investigate the long run relationship between export demand, and its determinants, namely income and relative prices. As a result of this empirical analysis, it was demonstrated that export volume, income and relative prices were cointegrated. The estimated long term elasticities of export demand with respect to income and relative prices are 0.21 and -1.684, respectively. The sum of the elasticities of import and export demand exceeds one (-1.01 i.e., Marshall-Lerner condition holds. These results show that monetary, fiscal and exchange rate policies may be used as substitutive policies to correct unfavourable trade balance.
Cointegration-based financial networks study in Chinese stock market
Tu, Chengyi
2014-05-01
We propose a method based on cointegration instead of correlation to construct financial complex network in Chinese stock market. The network is obtained starting from the matrix of p-value calculated by Engle-Granger cointegration test between all pairs of stocks. Then some tools for filtering information in complex network are implemented to prune the complete graph described by the above matrix, such as setting a level of statistical significance as a threshold and Planar Maximally Filtered Graph. We also calculate Partial Correlation Planar Graph of these stocks to compare the above networks. Last, we analyze these directed, weighted and non-symmetric networks by using standard methods of network analysis, including degree centrality, PageRank, HITS, local clustering coefficient, K-shell and strongly and weakly connected components. The results shed a new light on the underlying mechanisms and driving forces in a financial market and deepen our understanding of financial complex network.
Directory of Open Access Journals (Sweden)
Ibraheem Kamaldeen Nageri
2015-11-01
Full Text Available This paper studies the effect of capital market on economic growth in the presence of corruption in the Nigerian context. We employed the use of cointegration and Vector Error Correction Model (VECM. We find out that both corruption and capital market has long run associationship with economic development in Nigeria but has no short run relationship. This simply means that there is short run gain and long run pain for the Nigerian economy if corruption and capital market are not checked and well regulated respectively in Nigeria. We therefore recommend that government should strengthen the anti-graft agencies and equip them technologically and make them independent, educate the public on the problems associated with corrupt practices and the economic implication especially through the capital market and encourage local investors to invest in the capital market to improve liquidity and profitability of the Nigerian capital market.
Directory of Open Access Journals (Sweden)
Olumuyiwa Tolulope APANISILE
2014-12-01
Full Text Available The study examines the effect of military expenditure on output in Nigeria both in the short-run and in the long-run period. In addition, it verified whether military expenditure is an economically non-contributive activity using ARDL bounds testing approach to co-integration. Results showed that military spending has negative and significant effect on output in the short-run but positive and significant effect in the long-run. Labour and capital have positive and significant effects both in the long-run and short-run. In addition, labour has the highest coefficient (3.0709 in the long-run.The study concludes that government should reduce its expenditure on defense and concentrate more on human capital development, since military spending contributes nothing to output in the short-run.
Xiao, Qiang; Gao, Yang; Hu, Dan; Tan, Hong; Wang, Tianxiang
2011-07-01
We have investigated the interactions between economic growth and industrial wastewater discharge from 1978 to 2007 in China's Hunan Province using co-integration theory and an error-correction model. Two main economic growth indicators and four representative industrial wastewater pollutants were selected to demonstrate the interaction mechanism. We found a long-term equilibrium relationship between economic growth and the discharge of industrial pollutants in wastewater between 1978 and 2007 in Hunan Province. The error-correction mechanism prevented the variable expansion for long-term relationship at quantity and scale, and the size of the error-correction parameters reflected short-term adjustments that deviate from the long-term equilibrium. When economic growth changes within a short term, the discharge of pollutants will constrain growth because the values of the parameters in the short-term equation are smaller than those in the long-term co-integrated regression equation, indicating that a remarkable long-term influence of economic growth on the discharge of industrial wastewater pollutants and that increasing pollutant discharge constrained economic growth. Economic growth is the main driving factor that affects the discharge of industrial wastewater pollutants in Hunan Province. On the other hand, the discharge constrains economic growth by producing external pressure on growth, although this feedback mechanism has a lag effect. Economic growth plays an important role in explaining the predicted decomposition of the variance in the discharge of industrial wastewater pollutants, but this discharge contributes less to predictions of the variations in economic growth.
International Nuclear Information System (INIS)
Storlie, Curtis B.; Swiler, Laura P.; Helton, Jon C.; Sallaberry, Cedric J.
2009-01-01
The analysis of many physical and engineering problems involves running complex computational models (simulation models, computer codes). With problems of this type, it is important to understand the relationships between the input variables (whose values are often imprecisely known) and the output. The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model. In this presentation, an improvement on existing methods for SA of complex computer models is described for use when the model is too computationally expensive for a standard Monte-Carlo analysis. In these situations, a meta-model or surrogate model can be used to estimate the necessary sensitivity index for each input. A sensitivity index is a measure of the variance in the response that is due to the uncertainty in an input. Most existing approaches to this problem either do not work well with a large number of input variables and/or they ignore the error involved in estimating a sensitivity index. Here, a new approach to sensitivity index estimation using meta-models and bootstrap confidence intervals is described that provides solutions to these drawbacks. Further, an efficient yet effective approach to incorporate this methodology into an actual SA is presented. Several simulated and real examples illustrate the utility of this approach. This framework can be extended to uncertainty analysis as well.
Czech Academy of Sciences Publication Activity Database
Brázdik, František
-, č. 286 (2006), s. 1-45 ISSN 1211-3298 R&D Projects: GA MŠk LC542 Institutional research plan: CEZ:AV0Z70850503 Keywords : rice farms * data envelopment analysis Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp286.pdf
Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A
2015-05-01
Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.
Wang, Su-Chen; Tsai, Chi-Cheng; Huang, Shun-Te; Hong, Yu-Jue
2002-12-01
Data envelopment analysis (DEA), a cross-sectional study design based on secondary data analysis, was used to evaluate the relative operational efficiency of 16 dental departments in medical centers in Taiwan in 1999. The results indicated that 68.7% of all dental departments in medical centers had poor performance in terms of overall efficiency and scale efficiency. All relatively efficient dental departments were in private medical centers. Half of these dental departments were unable to fully utilize available medical resources. 75.0% of public medical centers did not take full advantage of medical resources at their disposal. In the returns to scale, 56.3% of dental departments in medical centers exhibited increasing returns to scale, due to the insufficient scale influencing overall hospital operational efficiency. Public medical centers accounted for 77.8% of the institutions affected. The scale of dental departments in private medical centers was more appropriate than those in public medical centers. In the sensitivity analysis, the numbers of residents, interns, and published papers were used to assess teaching and research. Greater emphasis on teaching and research in medical centers has a large effect on the relative inefficiency of hospital operation. Dental departments in private medical centers had a higher mean overall efficiency score than those in public medical centers, and the overall efficiency of dental departments in non-university hospitals was greater than those in university hospitals. There was no information to evaluate the long-term efficiency of each dental department in all hospitals. A different combination of input and output variables, using common multipliers for efficiency value measurements in DEA, may help establish different pioneering dental departments in hospitals.
Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran
Ahani, Hossien; Kherad, Mehrzad; Kousari, Mohammad Reza; van Roosmalen, Lieke; Aryanfar, Ramin; Hosseini, Seyyed Mashaallah
2013-05-01
Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955-2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann-Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.
Directory of Open Access Journals (Sweden)
Ignacio Santamaría
2008-04-01
Full Text Available This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.
Directory of Open Access Journals (Sweden)
Shanshan eLi
2016-01-01
Full Text Available Independent Component analysis (ICA is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.
A critique of non-parametric efficiency analysis in energy economics studies
International Nuclear Information System (INIS)
Chen, Chien-Ming
2013-01-01
The paper reexamines non-additive environmental efficiency models with weakly-disposable undesirable outputs appeared in the literature of energy economics. These efficiency models are used in numerous studies published in this journal and other energy-related outlets. Recent studies, however, have found key limitations of the weak-disposability assumption in its application to environmental efficiency analysis. It is found that efficiency scores obtained from non-additive efficiency models can be non-monotonic in pollution quantities under the weak-disposability assumption — which is against common intuition and the principle of environmental economics. In this paper, I present taxonomy of efficiency models found in the energy economics literature and illustrate the above limitations and discuss implications of monotonicity from a practical viewpoint. Finally, I review the formulations for a variable returns-to-scale technology with weakly-disposable undesirable outputs, which has been misused in a number of papers in the energy economics literature. An application to evaluating the energy efficiencies of 23 European Union states is presented to illustrate the problem. - Highlights: • Review different environmental efficiency model used in energy economics studies • Highlight limitations of these environmental efficiency models • These limitations have not been recognized in the existing energy economics literature. • Data from 23 European Union states are used to illustrate the methodological consequences
Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis
International Nuclear Information System (INIS)
Wu, F.; Fan, L.W.; Zhou, P.; Zhou, D.Q.
2012-01-01
Global awareness on energy security and climate change has created much interest in assessing economy-wide energy efficiency performance. A number of previous studies have contributed to evaluate energy efficiency performance using different analytical techniques among which data envelopment analysis (DEA) has recently received increasing attention. Most of DEA-related energy efficiency studies do not consider undesirable outputs such as CO 2 emissions in their modeling framework, which may lead to biased energy efficiency values. Within a joint production framework of desirable and undesirable outputs, in this paper we construct both static and dynamic energy efficiency performance indexes for measuring industrial energy efficiency performance by using several environmental DEA models with CO 2 emissions. The dynamic energy efficiency performance indexes have further been decomposed into two contributing components. We finally apply the indexes proposed to assess the industrial energy efficiency performance of different provinces in China over time. Our empirical study shows that the energy efficiency improvement in China's industrial sector was mainly driven by technological improvement. - Highlights: ► China's industrial energy efficiency is evaluated by DEA models with CO 2 emissions. ► China's industrial energy efficiency improved by 5.6% annually since 1997. ► Industrial energy efficiency improvement in China was mainly driven by technological improvement.
Energy Technology Data Exchange (ETDEWEB)
Ford, Eric B.; /Florida U.; Fabrycky, Daniel C.; /Lick Observ.; Steffen, Jason H.; /Fermilab; Carter, Joshua A.; /Harvard-Smithsonian Ctr. Astrophys.; Fressin, Francois; /Harvard-Smithsonian Ctr. Astrophys.; Holman, Matthew J.; /Harvard-Smithsonian Ctr. Astrophys.; Lissauer, Jack J.; /NASA, Ames; Moorhead, Althea V.; /Florida U.; Morehead, Robert C.; /Florida U.; Ragozzine, Darin; /Harvard-Smithsonian Ctr. Astrophys.; Rowe, Jason F.; /NASA, Ames /SETI Inst., Mtn. View /San Diego State U., Astron. Dept.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies are in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the transit timing variations of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
International Nuclear Information System (INIS)
Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C.; Fabrycky, Daniel C.; Steffen, Jason H.; Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David; Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A.; Welsh, William F.; Allen, Christopher; Batalha, Natalie M.; Buchhave, Lars A.
2012-01-01
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Energy Technology Data Exchange (ETDEWEB)
Ford, Eric B.; Moorhead, Althea V.; Morehead, Robert C. [Astronomy Department, University of Florida, 211 Bryant Space Sciences Center, Gainesville, FL 32611 (United States); Fabrycky, Daniel C. [UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); Steffen, Jason H. [Fermilab Center for Particle Astrophysics, P.O. Box 500, MS 127, Batavia, IL 60510 (United States); Carter, Joshua A.; Fressin, Francois; Holman, Matthew J.; Ragozzine, Darin; Charbonneau, David [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Lissauer, Jack J.; Rowe, Jason F.; Borucki, William J.; Bryson, Stephen T.; Burke, Christopher J.; Caldwell, Douglas A. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Welsh, William F. [Astronomy Department, San Diego State University, San Diego, CA 92182-1221 (United States); Allen, Christopher [Orbital Sciences Corporation/NASA Ames Research Center, Moffett Field, CA 94035 (United States); Batalha, Natalie M. [Department of Physics and Astronomy, San Jose State University, San Jose, CA 95192 (United States); Buchhave, Lars A., E-mail: eford@astro.ufl.edu [Niels Bohr Institute, Copenhagen University, DK-2100 Copenhagen (Denmark); Collaboration: Kepler Science Team; and others
2012-05-10
We present a new method for confirming transiting planets based on the combination of transit timing variations (TTVs) and dynamical stability. Correlated TTVs provide evidence that the pair of bodies is in the same physical system. Orbital stability provides upper limits for the masses of the transiting companions that are in the planetary regime. This paper describes a non-parametric technique for quantifying the statistical significance of TTVs based on the correlation of two TTV data sets. We apply this method to an analysis of the TTVs of two stars with multiple transiting planet candidates identified by Kepler. We confirm four transiting planets in two multiple-planet systems based on their TTVs and the constraints imposed by dynamical stability. An additional three candidates in these same systems are not confirmed as planets, but are likely to be validated as real planets once further observations and analyses are possible. If all were confirmed, these systems would be near 4:6:9 and 2:4:6:9 period commensurabilities. Our results demonstrate that TTVs provide a powerful tool for confirming transiting planets, including low-mass planets and planets around faint stars for which Doppler follow-up is not practical with existing facilities. Continued Kepler observations will dramatically improve the constraints on the planet masses and orbits and provide sensitivity for detecting additional non-transiting planets. If Kepler observations were extended to eight years, then a similar analysis could likely confirm systems with multiple closely spaced, small transiting planets in or near the habitable zone of solar-type stars.
Directory of Open Access Journals (Sweden)
Navid Haghighat
2017-12-01
Full Text Available This paper focuses on evaluating airline service quality from the perspective of passengers' view. Until now a lot of researches has been performed in airline service quality evaluation in the world but a little research has been conducted in Iran, yet. In this study, a framework for measuring airline service quality in Iran is proposed. After reviewing airline service quality criteria, SSQAI model was selected because of its comprehensiveness in covering airline service quality dimensions. SSQAI questionnaire items were redesigned to adopt with Iranian airlines requirements and environmental circumstances in the Iran's economic and cultural context. This study includes fuzzy decision-making theory, considering the possible fuzzy subjective judgment of the evaluators during airline service quality evaluation. Fuzzy TOPSIS have been applied for ranking airlines service quality performances. Three major Iranian airlines which have the most passenger transfer volumes in domestic and foreign flights were chosen for evaluation in this research. Results demonstrated Mahan airline has got the best service quality performance rank in gaining passengers' satisfaction with delivery of high-quality services to its passengers, among the three major Iranian airlines. IranAir and Aseman airlines placed in the second and third rank, respectively, according to passenger's evaluation. Statistical analysis has been used in analyzing passenger responses. Due to the abnormality of data, Non-parametric tests were applied. To demonstrate airline ranks in every criterion separately, Friedman test was performed. Variance analysis and Tukey test were applied to study the influence of increasing in age and educational level of passengers on degree of their satisfaction from airline's service quality. Results showed that age has no significant relation to passenger satisfaction of airlines, however, increasing in educational level demonstrated a negative impact on
On Cooper's Nonparametric Test.
Schmeidler, James
1978-01-01
The basic assumption of Cooper's nonparametric test for trend (EJ 125 069) is questioned. It is contended that the proper assumption alters the distribution of the statistic and reduces its usefulness. (JKS)
DEFF Research Database (Denmark)
Tan, Qihua; Zhao, J H; Iachine, I
2004-01-01
This report investigates the power issue in applying the non-parametric linkage analysis of affected sib-pairs (ASP) [Kruglyak and Lander, 1995: Am J Hum Genet 57:439-454] to localize genes that contribute to human longevity using long-lived sib-pairs. Data were simulated by introducing a recently...... developed statistical model for measuring marker-longevity associations [Yashin et al., 1999: Am J Hum Genet 65:1178-1193], enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated...... in case of a dominant effect. Although the power issue may depend heavily on the true genetic nature in maintaining survival, our study suggests that results from small-scale sib-pair investigations should be referred with caution, given the complexity of human longevity....
Davies, Patrick Laurie
2014-01-01
Introduction IntroductionApproximate Models Notation Two Modes of Statistical AnalysisTowards One Mode of Analysis Approximation, Randomness, Chaos, Determinism ApproximationA Concept of Approximation Approximation Approximating a Data Set by a Model Approximation Regions Functionals and EquivarianceRegularization and Optimality Metrics and DiscrepanciesStrong and Weak Topologies On Being (almost) Honest Simulations and Tables Degree of Approximation and p-values ScalesStability of Analysis The Choice of En(α, P) Independence Procedures, Approximation and VaguenessDiscrete Models The Empirical Density Metrics and Discrepancies The Total Variation Metric The Kullback-Leibler and Chi-Squared Discrepancies The Po(λ) ModelThe b(k, p) and nb(k, p) Models The Flying Bomb Data The Student Study Times Data OutliersOutliers, Data Analysis and Models Breakdown Points and Equivariance Identifying Outliers and Breakdown Outliers in Multivariate Data Outliers in Linear Regression Outliers in Structured Data The Location...
Directory of Open Access Journals (Sweden)
Archer Kellie J
2008-02-01
Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been
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...
Cointegration rank testing under conditional heteroskedasticity
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.
2010-01-01
We analyze the properties of the conventional Gaussian-based cointegrating rank tests of Johansen (1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models) in the case where the vector of series under test is driven by globally stationary, conditionally heteroskedastic......, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples under a variety of conditionally heteroskedastic innovation processes. An empirical application to the term structure of interest rates is given....
Introduction to nonparametric statistics for the biological sciences using R
MacFarland, Thomas W
2016-01-01
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses a...
Lu, Tao
2016-01-01
The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.
Nonparametric Transfer Function Models
Liu, Jun M.; Chen, Rong; Yao, Qiwei
2009-01-01
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. PMID:20628584
Dickhaus, Thorsten
2018-01-01
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Directory of Open Access Journals (Sweden)
Mahmudul Mannan Toy
2011-01-01
Full Text Available The broad objective of this study is to empirically estimate the export supply model of Bangladesh. The techniques of cointegration, Engle-Granger causality and Vector Error Correction are applied to estimate the export supply model. The econometric analysis is done by using the time series data of the variables of interest which is collected from various secondary sources. The study has empirically tested the hypothesis, long run relationship and casualty between variables of the model. The cointegration analysis shows that all the variables of the study are co-integrated at their first differences meaning that there exists long run relationship among the variables. The VECM estimation shows the dynamics of variables in the export supply function and the short run and long run elasticities of export supply with respect to each independent variable. The error correction term is found negative which indicates that any short run disequilibrium will be turned into equilibrium in the long run.
Febrian, Erie; Herwany, Aldrin
2007-01-01
For both risk management and portfolio selection purposes, modeling the linkage across financial markets is crucial, especially among neighboring stock markets. In investigating the dependence or co-movement of three or more stock markets in different countries, researchers frequently use co-integration and causality analysis. Nevertheless, they conducted the causality in mean tests but not the causality in variance tests. This paper examines the co-integration and causal relations among ...
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....
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....
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....
An Empirical Investigation of the Ohlson Model–A Panel Cointegration Approach
Directory of Open Access Journals (Sweden)
Shih-Cheng Lee
2014-07-01
Full Text Available This paper uses a panel cointegration method to test the Ohlson (1995 model. Sample firms are selected from US listed companies during the period from 1986 to 2004. The analysis is focussed on whether the fundamental value of corporations cointegrates with market value. The results support the hypothesis of cointegration that a long-run equilibrium relationship exists between a corporation’s fundamental value and market value. Subsequently, this paper evaluates the predictive power of the Ohlson model for future market value assessment. Since the Ohlson model is built upon the dividend discount model, this paper also examines the validity and the predictive power of the dividend discount model as a basis for comparison. The results show that the Ohlson model can forecast future stock price movements much more accurately in any predicted horizon.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Energy Technology Data Exchange (ETDEWEB)
Peterson, James T.
1999-12-01
Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network.
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Carlini, Federico; Santucci de Magistris, Paolo
for any choice of the lag length, also when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......) and it is a generalization to the fractional case of the I(1) condition in the VECM model. The assessment of the F(d) condition in the empirical analysis is relevant for the determination of the fractional parameters as well as the number of lags. The paper also illustrates the indeterminacy between the cointegration rank...
Energy consumption and GDP in Turkey : Is there a co-integration relationship?
Montfort, van K.; Lise, W.
2007-01-01
Energy consumption and GDP are expected to grow by 5.9% and 7% annually until 2025 in Turkey. This paper tries to unfold the linkage between energy consumption and GDP by undertaking a co-integration analysis for Turkey with annual data over the period 1970-2003. The analysis shows that energy
Energy consumption and GDP in Turkey: is there a co-integration relationship?
van Montfort, C.A.G.M.; Lise, W.
2007-01-01
Energy consumption and GDP are expected to grow by 5.9% and 7% annually until 2025 in Turkey. This paper tries to unfold the linkage between energy consumption and GDP by undertaking a co-integration analysis for Turkey with annual data over the period 1970-2003. The analysis shows that energy
Lee, L.; Helsel, D.
2007-01-01
Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
Measuring real exchange rate misalignment in Croatia: cointegration approach
Directory of Open Access Journals (Sweden)
Irena Palić
2014-12-01
Full Text Available The purpose of the paper is to analyze misalignment of the real exchange rate in Croatia. The misalignment analysis is conducted using the permanent equilibrium exchange rate approach. The equilibrium real exchange rate is computed using the cointegration approach whereby the real exchange rate and its fundamentals, namely terms of trade, net foreign assets and the ratio of prices of tradables to non-tradables are included in cointegration analysis. The Hodrick and Prescott filter is used to obtain permanent values of the equilibrium real exchange rate. The real exchange rate misalignment is computed as the deviation of the RER from its permanent equilibrium level. Four overvaluation periods and three undervaluation periods are recorded in Croatia in the observed period. Overvaluation periods are more often and of longer duration than undervaluation periods. However, the real exchange rate does not deviate largely from its estimated equilibrium value in the observed period, and it is neither overvalued nor undervalued constantly, but the periods alternate. Considering the results of the analysis, together with the empirical characteristics of Croatian economy, namely the high foreign currency indebtedness, highly euroized economy and underdeveloped export oriented sector, the depreciation of the real exchange rate is not recommended to economic policy makers and the current Croatian exchange rate policy is appropriate.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Quantal Response: Nonparametric Modeling
2017-01-01
capture the behavior of observed phenomena. Higher-order polynomial and finite-dimensional spline basis models allow for more complicated responses as the...flexibility as these are nonparametric (not constrained to any particular functional form). These should be useful in identifying nonstandard behavior via... deviance ∆ = −2 log(Lreduced/Lfull) is defined in terms of the likelihood function L. For normal error, Lfull = 1, and based on Eq. A-2, we have log
Long term economic relationships from cointegration maps
Vicente, Renato; Pereira, Carlos de B.; Leite, Vitor B. P.; Caticha, Nestor
2007-07-01
We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the sorting points into neighborhoods (SPIN) technique, which has been previously used to analyze large data sets from DNA arrays. We exemplify the technique in three data sets: US interest rates (USIR), monthly inflation rates and gross domestic product (GDP) growth rates.
Cointegration and why it works for SHM
Cross, Elizabeth J.; Worden, Keith
2012-08-01
One of the most fundamental problems in Structural Health Monitoring (SHM) is that of projecting out operational and environmental variations from measured feature data. The reason for this is that algorithms used for SHM to detect changes in structural condition should not raise alarms if the structure of interest changes because of benign operational or environmental variations. This is sometimes called the data normalisation problem. Many solutions to this problem have been proposed over the years, but a new approach that uses cointegration, a concept from the field of econometrics, appears to provide a very promising solution. The theory of cointegration is mathematically complex and its use is based on the holding of a number of assumptions on the time series to which it is applied. An interesting observation that has emerged from its applications to SHM data is that the approach works very well even though the aforementioned assumptions do not hold in general. The objective of the current paper is to discuss how the cointegration assumptions break down individually in the context of SHM and to explain why this does not invalidate the application of the algorithm.
R. Paap (Richard); H.K. van Dijk (Herman)
2002-01-01
textabstractStylized facts show that average growth rates of US per capita consumption and income differ in recession and expansion periods. Since a linear combination of such series does not have to be a constant mean process, standard cointegration analysis between the variables to examine the
The effects of additive outliers on tests for unit roots and cointegration
Ph.H.B.F. Franses (Philip Hans); N. Haldrup (Niels)
1994-01-01
textabstractThe properties of the univariate Dickey-Fuller test and the Johansen test for the cointegrating rank when there exist additive outlying observations in the time series are examined. The analysis provides analytical as well as numerical evidence that additive outliers may produce spurious
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....
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....
Afshinpour, Babak; Hossein-Zadeh, Gholam-Ali; Soltanian-Zadeh, Hamid
2008-06-30
Unknown low frequency fluctuations called "trend" are observed in noisy time-series measured for different applications. In some disciplines, they carry primary information while in other fields such as functional magnetic resonance imaging (fMRI) they carry nuisance effects. In all cases, however, it is necessary to estimate them accurately. In this paper, a method for estimating trend in the presence of fractal noise is proposed and applied to fMRI time-series. To this end, a partly linear model (PLM) is fitted to each time-series. The parametric and nonparametric parts of PLM are considered as contributions of hemodynamic response and trend, respectively. Using the whitening property of wavelet transform, the unknown components of the model are estimated in the wavelet domain. The results of the proposed method are compared to those of other parametric trend-removal approaches such as spline and polynomial models. It is shown that the proposed method improves activation detection and decreases variance of the estimated parameters relative to the other methods.
Directory of Open Access Journals (Sweden)
Lars Ängquist
2008-01-01
Full Text Available In this article we try to discuss nonparametric linkage (NPL score functions within a broad and quite general framework. The main focus of the paper is the structure, derivation principles and interpretations of the score function entity itself. We define and discuss several families of one-locus score function definitions, i.e. the implicit, explicit and optimal ones. Some generalizations and comments to the two-locus, unconditional and conditional, cases are included as well. Although this article mainly aims at serving as an overview, where the concept of score functions are put into a covering context, we generalize the noncentrality parameter (NCP optimal score functions in Ängquist et al. (2007 to facilitate—through weighting—for incorporation of several plausible distinct genetic models. Since the genetic model itself most oftenly is to some extent unknown this facilitates weaker prior assumptions with respect to plausible true disease models without loosing the property of NCP-optimality. Moreover, we discuss general assumptions and properties of score functions in the above sense. For instance, the concept of identical by descent (IBD sharing structures and score function equivalence are discussed in some detail.
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Vangelis Sakkalis
2008-01-01
Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
International Nuclear Information System (INIS)
Wei, Chu; Löschel, Andreas; Liu, Bing
2015-01-01
In the context of soaring demand for electricity, mitigating and controlling greenhouse gas emissions is a great challenge for China's power sector. Increasing attention has been placed on the evaluation of energy efficiency and CO 2 abatement potential in the power sector. However, studies at the micro-level are relatively rare due to serious data limitations. This study uses the 2004 and 2008 Census data of Zhejiang province to construct a non-parametric frontier in order to assess the abatement space of energy and associated CO 2 emission from China's coal-fired power enterprises. A Weighted Russell Directional Distance Function (WRDDF) is applied to construct an energy-saving potential index and a CO 2 emission-abatement potential index. Both indicators depict the inefficiency level in terms of energy utilization and CO 2 emissions of electric power plants. Our results show a substantial variation of energy-saving potential and CO 2 abatement potential among enterprises. We find that large power enterprises are less efficient in 2004, but become more efficient than smaller enterprises in 2008. State-owned enterprises (SOE) are not significantly different in 2008 from 2004, but perform better than their non-SOE counterparts in 2008. This change in performance for large enterprises and SOE might be driven by the “top-1000 Enterprise Energy Conservation Action” that was implemented in 2006. - Highlights: • Energy-saving potential and CO 2 abatement-potential for Chinese power enterprise are evaluated. • The potential to curb energy and emission shows great variation and dynamic changes. • Large enterprise is less efficient than small enterprise in 2004, but more efficient in 2008. • The state-owned enterprise performs better than non-state-owned enterprise in 2008
Directory of Open Access Journals (Sweden)
Fernandez Ana
2010-05-01
Full Text Available Abstract Background Previous studies have analyzed the psychometric properties of the World Health Organization Disability Assessment Schedule II (WHO-DAS II using classical omnibus measures of scale quality. These analyses are sample dependent and do not model item responses as a function of the underlying trait level. The main objective of this study was to examine the effectiveness of the WHO-DAS II items and their options in discriminating between changes in the underlying disability level by means of item response analyses. We also explored differential item functioning (DIF in men and women. Methods The participants were 3615 adult general practice patients from 17 regions of Spain, with a first diagnosed major depressive episode. The 12-item WHO-DAS II was administered by the general practitioners during the consultation. We used a non-parametric item response method (Kernel-Smoothing implemented with the TestGraf software to examine the effectiveness of each item (item characteristic curves and their options (option characteristic curves in discriminating between changes in the underliying disability level. We examined composite DIF to know whether women had a higher probability than men of endorsing each item. Results Item response analyses indicated that the twelve items forming the WHO-DAS II perform very well. All items were determined to provide good discrimination across varying standardized levels of the trait. The items also had option characteristic curves that showed good discrimination, given that each increasing option became more likely than the previous as a function of increasing trait level. No gender-related DIF was found on any of the items. Conclusions All WHO-DAS II items were very good at assessing overall disability. Our results supported the appropriateness of the weights assigned to response option categories and showed an absence of gender differences in item functioning.
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...
Portfolio implications of cointegration between labor income and dividends
de Jong, F.C.J.M.
2012-01-01
This paper analyzes the implications of cointegration between labor income and dividends for the optimal portfolio weight for stocks. In a recent paper, Benzoni et al. (J Finance 62:2123–2167, 2007) claim that, as a result of cointegration, the optimal weight in stocks may be smaller for young
Correlation, Regression, and Cointegration of Nonstationary Economic Time Series
DEFF Research Database (Denmark)
Johansen, Søren
), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population...... values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient......Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974...
Nonparametric combinatorial sequence models.
Wauthier, Fabian L; Jordan, Michael I; Jojic, Nebojsa
2011-11-01
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This article presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three biological sequence families which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution over sequence representations induced by the prior. By integrating out the posterior, our method compares favorably to leading binding predictors.
A Structural Labor Supply Model with Nonparametric Preferences
van Soest, A.H.O.; Das, J.W.M.; Gong, X.
2000-01-01
Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis.This paper presents an example where nonparametric flexibility can be attained
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Nonparametric tests for censored data
Bagdonavicus, Vilijandas; Nikulin, Mikhail
2013-01-01
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
Nonparametric statistics with applications to science and engineering
Kvam, Paul H
2007-01-01
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provide...
Nonparametric e-Mixture Estimation.
Takano, Ken; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru
2016-12-01
This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.
Petrović, Predrag
2014-01-01
The paper shows the results of an empirical analysis of the relative variant of purchasing power parity (PPP) for dinar-euro and dinar-dollar ex-change rates. The study was conducted for the period January 2007 - August 2013 and involved testing of the empirical foundation of strong and weak PPP forms. The first part of the strong PPP form testing comes down to examination of non-stationarity of dinar-euro and dinar-dollar real exchange rates by use of standard unit root tests (ADF, PP, KPSS,...
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Miletić Mirjana
2012-01-01
Full Text Available This paper aims to reassess the contribution of the Balassa-Samuelson effect to the inflation and real exchange rate appreciation using panel data for nine CEECs covering the period ranging from the mid-1990s to the third quarter of 2010. The main idea of this analysis is to answer the question of whether the Global Economic Crisis had a significant impact on the efforts of CEECs to stay on the path of real convergence. The Balassa-Samuelson effect explains less than 1.5 percentage points on average of inflation differential relative to the euro area and around 1 percentage point of the total domestic inflation. The results are robust across the model specification and estimation method. Most of the results point out that the Balassa-Samuelson effect has not changed considerably during the crisis even though it is lower compared to that in the earlier stage of transition (for the period up to 2004.
2nd Conference of the International Society for Nonparametric Statistics
Manteiga, Wenceslao; Romo, Juan
2016-01-01
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...
Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng
2018-03-01
Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.
Cointegration Approach to Analysing Inflation in Croatia
Directory of Open Access Journals (Sweden)
Lena Malešević-Perović
2009-06-01
Full Text Available The aim of this paper is to analyse the determinants of inflation in Croatia in the period 1994:6-2006:6. We use a cointegration approach and find that increases in wages positively influence inflation in the long-run. Furthermore, in the period from June 1994 onward, the depreciation of the currency also contributed to inflation. Money does not explain Croatian inflation. This irrelevance of the money supply is consistent with its endogeneity to exchange rate targeting, whereby the money supply is determined by developments in the foreign exchange market. The value of inflation in the previous period is also found to be significant, thus indicating some inflation inertia.
Varabyova, Yauheniya; Schreyögg, Jonas
2013-09-01
There is a growing interest in the cross-country comparisons of the performance of national health care systems. The present work provides a comparison of the technical efficiency of the hospital sector using unbalanced panel data from OECD countries over the period 2000-2009. The estimation of the technical efficiency of the hospital sector is performed using nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Internal and external validity of findings is assessed by estimating the Spearman rank correlations between the results obtained in different model specifications. The panel-data analyses using two-step DEA and one-stage SFA show that countries, which have higher health care expenditure per capita, tend to have a more technically efficient hospital sector. Whether the expenditure is financed through private or public sources is not related to the technical efficiency of the hospital sector. On the other hand, the hospital sector in countries with higher income inequality and longer average hospital length of stay is less technically efficient. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Angelis, Luca De; Rahbek, Anders
2015-01-01
In this article, we investigate the behaviour of a number of methods for estimating the co-integration rank in VAR systems characterized by heteroskedastic innovation processes. In particular, we compare the efficacy of the most widely used information criteria, such as Akaike Information Criterion....... The relative finite-sample properties of the different methods are investigated by means of a Monte Carlo simulation study. For the simulation DGPs considered in the analysis, we find that the BIC-based procedure and the bootstrap sequential test procedure deliver the best overall performance in terms......-based method to over-estimate the co-integration rank in relatively small sample sizes....
Nonparametric identification of copula structures
Li, Bo; Genton, Marc G.
2013-01-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric
International parity relations between Poland and Germany: a cointegrated VAR approach
Stazka, Agnieszka
2008-01-01
This paper analyses empirically the purchasing power parity, the uncovered interest parity and the real interest parity (Fisher parity) between Poland and Germany. The international parity relations are investigated jointly within the cointegrated VAR framework. Our analysis fails to find evidence that the parities, or any linear combinations of them, hold for our data set. We identify two long-run equilibrium relations: one imposing a long-run homogeneity restriction on the domestic (i.e. Po...
International Nuclear Information System (INIS)
Lam, G.K.
1989-01-01
The combined effects of mixed radiations can be examined using a system of simple isoeffect relations which are derived from a recent analysis of in vitro results obtained for a variety of radiation mixtures. Similar isoeffect analysis methods have been used for over two decades in studies of the combined action of toxic agents such as drugs and antibiotics. Because of the isoeffect approach, the method is particularly useful for the analysis of ordinal data for which conventional models that are based on parametric dose-effect relations may not be suitable. This is illustrated by applying the method to the analysis of a set of recently published in vivo data using the mouse foot skin reaction system for mixtures of neutrons and X rays. The good agreement between this method and the ordinal data also helps to provide further experimental support for the existence of a class of radiobiological data for which the simple isoeffect relations are valid
A cointegration analysis of petroleum futures prices
Energy Technology Data Exchange (ETDEWEB)
Serletis, Apostolos (Calgary Univ., AB (Canada). Dept. of Economics)
1994-04-01
This paper presents evidence concerning the number of common stochastic trends in a system of three petroleum futures prices (crude oil, heating oil and unleaded gasoline) using daily data from 3 December 1984 to 30 April 1993. Johansen's maximum likelihood approach for estimating long-run relations in multivariate vector autoregressive models is used. The results indicate the presence of only one common trend. (author)
Vertical price leadership : a cointegration analysis
Kuiper, W.E.; Meulenberg, M.T.G.
2002-01-01
Here we detail a method to test whether or not retailers allow suppliers to set the wholesale price not only on the basis of the costs faced by the suppliers but also on the basis of consumer demand. Using standard theory, long-run price relationships between the stages in the channel are derived.
A cointegration analysis of wine stock indexes
Sabina Introvigne; Emanuele Bacchiocchi; Daniela Vandone
2017-01-01
This paper analyzes price patterns and long-run relationships for both fine wine and non-fine wine, with the aim to highlight price dynamics and co-movements between series, and to exploit potential diversification benefits. Data are from Liv-Ex 100 Fine Wine for fine wine, the Mediobanca Global Wine Industry Share Price for normal wine, and the MSCI World Index as a proxy of the overall stock market. Engle-Granger and Johansen tests were used to detect whether and to what extent the series c...
Directory of Open Access Journals (Sweden)
İsmet GÖÇER
2014-06-01
Full Text Available In this study effects of foreign direct investment on employment, analyzed with multiple structural breaks unit root test of Carrion-i-Silvestre et al. (2009, multiple structural breaks cointegration test of Maki (2012 and dynamic ordinary least squares method for Turkey, China and India by using 1980-2011 period data. According to the empirical findings; series are non-stationary in level and there is a cointegration relationship between series. As a result of the long run analysis; 10% increase of foreign direct investment leads to a decreases on the employment in Turkey by 0.3%while decreases in China and India respectively by 0.3% and 0.2%.
Directory of Open Access Journals (Sweden)
Dermody James J
2004-11-01
Full Text Available Abstract Background A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. Results We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation. Conclusions This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.
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César Merino Soto
2009-05-01
Full Text Available Resumen:La presente investigación hace un estudio psicométrico de un nuevo sistema de calificación de la Prueba Gestáltica del Bendermodificada para niños, que es el Sistema de Calificación Cualitativa (Brannigan y Brunner, 2002, en un muestra de 244 niñosingresantes a primer grado de primaria en cuatro colegios públicos, ubicados en Lima. El enfoque usado es un análisis noparamétrico de ítems mediante el programa Testgraf (Ramsay, 1991. Los resultados indican niveles apropiados deconsistencia interna, identificándose la unidimensionalidad, y el buen nivel discriminativo de las categorías de calificación deeste Sistema Cualitativo. No se hallaron diferencias demográficas respecto al género ni la edad. Se discuten los presenteshallazgos en el contexto del potencial uso del Sistema de Calificación Cualitativa y del análisis no paramétrico de ítems en lainvestigación psicométrica.AbstracThis research designs a psychometric study of a new scoring system of the Bender Gestalt test modified to children: it is theQualitative Scoring System (Brannigan & Brunner, 2002, in a sample of 244 first grade children of primary level, in four public school of Lima. The approach aplied is the nonparametric item analysis using The test graft computer program (Ramsay, 1991. Our findings point to good levels of internal consistency, unidimensionality and good discriminative level ofthe categories of scoring from the Qualitative Scoring System. There are not demographic differences between gender or age.We discuss our findings within the context of the potential use of the Qualitative Scoring System and of the nonparametricitem analysis approach in the psychometric research.
International Nuclear Information System (INIS)
Chen, Chin-Wei; Cote, Patrick; Ferrarese, Laura; West, Andrew A.; Peng, Eric W.
2010-01-01
We present photometric and structural parameters for 100 ACS Virgo Cluster Survey (ACSVCS) galaxies based on homogeneous, multi-wavelength (ugriz), wide-field SDSS (DR5) imaging. These early-type galaxies, which trace out the red sequence in the Virgo Cluster, span a factor of nearly ∼10 3 in g-band luminosity. We describe an automated pipeline that generates background-subtracted mosaic images, masks field sources and measures mean shapes, total magnitudes, effective radii, and effective surface brightnesses using a model-independent approach. A parametric analysis of the surface brightness profiles is also carried out to obtain Sersic-based structural parameters and mean galaxy colors. We compare the galaxy parameters to those in the literature, including those from the ACSVCS, finding good agreement in most cases, although the sizes of the brightest, and most extended, galaxies are found to be most uncertain and model dependent. Our photometry provides an external measurement of the random errors on total magnitudes from the widely used Virgo Cluster Catalog, which we estimate to be σ(B T )∼ 0.13 mag for the brightest galaxies, rising to ∼ 0.3 mag for galaxies at the faint end of our sample (B T ∼ 16). The distribution of axial ratios of low-mass ( d warf ) galaxies bears a strong resemblance to the one observed for the higher-mass ( g iant ) galaxies. The global structural parameters for the full galaxy sample-profile shape, effective radius, and mean surface brightness-are found to vary smoothly and systematically as a function of luminosity, with unmistakable evidence for changes in structural homology along the red sequence. As noted in previous studies, the ugriz galaxy colors show a nonlinear but smooth variation over a ∼7 mag range in absolute magnitude, with an enhanced scatter for the faintest systems that is likely the signature of their more diverse star formation histories.
Is There Any Sectoral Cointegration in Indonesia Equity Market?
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Aileen Clarissa Surya
2018-03-01
Full Text Available This research analyzes short and medium-run cointegration relationship among 9 sectoral indices in Indonesia equity market (JCI, using 2012-2016 weekly closing prices as the data. Researchers analyzed the relationship among these sectors using Johansen-Julius Cointegration Test and predict the causal relationship using Engle-Granger Causality and model the causalities using Vector Error Correction Model. Researchers findings based on the empirical results of Johansen cointegration tests are there is no cointegration in the short-run as the sector indices performance are caused by unique moving factors that affect all sectors differently. However, there is a medium run relationship among the sectors as they are moved by macroeconomic and political conditions towards the same direction. Other two methods, Engle-Granger and VECM, are also supporting the results from Johansen cointegration tests. The findings from this research can be useful as an insight for investors and fund managers in minimizing portfolio risk by using sectoral diversification, which based on the research can only be applied in the short run period.
A nonparametric mixture model for cure rate estimation.
Peng, Y; Dear, K B
2000-03-01
Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.
Directory of Open Access Journals (Sweden)
Stochl Jan
2012-06-01
Full Text Available Abstract Background Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Methods Scalability of data from 1 a cross-sectional health survey (the Scottish Health Education Population Survey and 2 a general population birth cohort study (the National Child Development Study illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. Results and conclusions After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items we show that all items from the 12-item General Health Questionnaire (GHQ-12 – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales. An illustration of ordinal item analysis
Stochl, Jan; Jones, Peter B; Croudace, Tim J
2012-06-11
Mokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires. Scalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale. After an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12)--when binary scored--were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech's "well-being" and "distress" clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Directory of Open Access Journals (Sweden)
Cem KADILAR
2004-01-01
Full Text Available This study presents a statistical analysis of the aggregated import demand behaviour for Turkey. In this study, a small sample of annual data from 1970 to 2002 were used to investigate the long run relationship between import demand, and its determinants, namely income and relative prices, by the 'bounds test' method of Pesaran et al (2001. By the results of this test, it was demonstrated that import volume, income and relative prices were cointegrated. The estimated long term elasticities of import demand with respect to income and relative prices were found as 0.37 and 0.67, respectively. As an absolute value, the sum of elasticities of import and export demand (-1.01 is greater than one. Consequently, we deduce that monetary, fiscal and exchange rate policies may be used as substitutive policies to arrange unfavourable trade balance. But they are not sufficient.
Retail fuel price adjustment in Germany: A threshold cointegration approach
International Nuclear Information System (INIS)
Asane-Otoo, Emmanuel; Schneider, Jan
2015-01-01
Consumers in Germany often complain that retail fuel prices usually adjust quickly to crude oil price increases than decreases and characterize this pricing pattern as market power exploitation. In this paper, we use both weekly national and daily city-specific (Berlin, Hamburg, Munich and Cologne) data to investigate the extent to which retail fuel prices in Germany adjust to changes in the international crude oil price. At the national level with weekly prices, we find positive asymmetries for both gasoline and diesel within the period 2003–2007, reflecting that retail prices react more swiftly to crude oil price increases than decreases. In contrast, for 2009–2013, we observe symmetric adjustment and negative asymmetry for retail diesel and gasoline prices, respectively. The city level analysis supports our findings in the latter time period. Thus, regulatory measures aimed at the retail fuel market over recent years seem to have been effective, and, contrary to consumers' perception, we find no evidence for excessive market power or collusion. - Highlights: • The paper examines the adjustment of German retail fuel (gasoline and diesel) prices to international crude oil price changes. • An error correction model with threshold cointegration is used to investigate the price dynamics. • The findings generally point to a competitive retail fuel pricing, notwithstanding the oligopolistic market structure
Nonparametric Inference for Periodic Sequences
Sun, Ying
2012-02-01
This article proposes a nonparametric method for estimating the period and values of a periodic sequence when the data are evenly spaced in time. The period is estimated by a "leave-out-one-cycle" version of cross-validation (CV) and complements the periodogram, a widely used tool for period estimation. The CV method is computationally simple and implicitly penalizes multiples of the smallest period, leading to a "virtually" consistent estimator of integer periods. This estimator is investigated both theoretically and by simulation.We also propose a nonparametric test of the null hypothesis that the data have constantmean against the alternative that the sequence of means is periodic. Finally, our methodology is demonstrated on three well-known time series: the sunspots and lynx trapping data, and the El Niño series of sea surface temperatures. © 2012 American Statistical Association and the American Society for Quality.
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
Observations from sums of random variables with a random number of summands, known as random, compound or stopped sums arise within many areas of engineering and science. Quite often it is desirable to infer properties of the distribution of the terms in the random sum. In the present paper we...... review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
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...
Numerical distribution functions of fractional unit root and cointegration tests
DEFF Research Database (Denmark)
MacKinnon, James G.; Nielsen, Morten Ørregaard
We calculate numerically the asymptotic distribution functions of likelihood ratio tests for fractional unit roots and cointegration rank. Because these distributions depend on a real-valued parameter, b, which must be estimated, simple tabulation is not feasible. Partly due to the presence...
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...
Direct cointegration testing in error-correction models
F.R. Kleibergen (Frank); H.K. van Dijk (Herman)
1994-01-01
textabstractAbstract An error correction model is specified having only exact identified parameters, some of which reflect a possible departure from a cointegration model. Wald, likelihood ratio, and Lagrange multiplier statistics are derived to test for the significance of these parameters. The
Exact Rational Expectations, Cointegration, and Reduced Rank Regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Exact rational expectations, cointegration, and reduced rank regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Exact rational expectations, cointegration, and reduced rank regression
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
2008-01-01
We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important role...
Directory of Open Access Journals (Sweden)
Steven M Carr
-stepping-stone biogeographic models, but not a simple 1-step trans-Atlantic model. Plots of the cumulative pairwise sequence difference curves among seals in each of the four populations provide continuous proxies for phylogenetic diversification within each. Non-parametric Kolmogorov-Smirnov (K-S tests of maximum pairwise differences between these curves indicates that the Greenland Sea population has a markedly younger phylogenetic structure than either the White Sea population or the two Northwest Atlantic populations, which are of intermediate age and homogeneous structure. The Monte Carlo and K-S assessments provide sensitive quantitative tests of within-species mitogenomic phylogeography. This is the first study to indicate that the White Sea and Greenland Sea populations have different population genetic histories. The analysis supports the hypothesis that Harp Seals comprises three genetically distinguishable breeding populations, in the White Sea, Greenland Sea, and Northwest Atlantic. Implications for an ice-dependent species during ongoing climate change are discussed.
Non-parametric smoothing of experimental data
International Nuclear Information System (INIS)
Kuketayev, A.T.; Pen'kov, F.M.
2007-01-01
Full text: Rapid processing of experimental data samples in nuclear physics often requires differentiation in order to find extrema. Therefore, even at the preliminary stage of data analysis, a range of noise reduction methods are used to smooth experimental data. There are many non-parametric smoothing techniques: interval averages, moving averages, exponential smoothing, etc. Nevertheless, it is more common to use a priori information about the behavior of the experimental curve in order to construct smoothing schemes based on the least squares techniques. The latter methodology's advantage is that the area under the curve can be preserved, which is equivalent to conservation of total speed of counting. The disadvantages of this approach include the lack of a priori information. For example, very often the sums of undifferentiated (by a detector) peaks are replaced with one peak during the processing of data, introducing uncontrolled errors in the determination of the physical quantities. The problem is solvable only by having experienced personnel, whose skills are much greater than the challenge. We propose a set of non-parametric techniques, which allows the use of any additional information on the nature of experimental dependence. The method is based on a construction of a functional, which includes both experimental data and a priori information. Minimum of this functional is reached on a non-parametric smoothed curve. Euler (Lagrange) differential equations are constructed for these curves; then their solutions are obtained analytically or numerically. The proposed approach allows for automated processing of nuclear physics data, eliminating the need for highly skilled laboratory personnel. Pursuant to the proposed approach is the possibility to obtain smoothing curves in a given confidence interval, e.g. according to the χ 2 distribution. This approach is applicable when constructing smooth solutions of ill-posed problems, in particular when solving
Nonparametric identification of copula structures
Li, Bo
2013-06-01
We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process.We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets. © 2013 American Statistical Association.
Application of nonparametric statistic method for DNBR limit calculation
International Nuclear Information System (INIS)
Dong Bo; Kuang Bo; Zhu Xuenong
2013-01-01
Background: Nonparametric statistical method is a kind of statistical inference method not depending on a certain distribution; it calculates the tolerance limits under certain probability level and confidence through sampling methods. The DNBR margin is one important parameter of NPP design, which presents the safety level of NPP. Purpose and Methods: This paper uses nonparametric statistical method basing on Wilks formula and VIPER-01 subchannel analysis code to calculate the DNBR design limits (DL) of 300 MW NPP (Nuclear Power Plant) during the complete loss of flow accident, simultaneously compared with the DL of DNBR through means of ITDP to get certain DNBR margin. Results: The results indicate that this method can gain 2.96% DNBR margin more than that obtained by ITDP methodology. Conclusions: Because of the reduction of the conservation during analysis process, the nonparametric statistical method can provide greater DNBR margin and the increase of DNBR margin is benefited for the upgrading of core refuel scheme. (authors)
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
Asymmetric cointegration between exchange rate and trade balance in Nigeria
Directory of Open Access Journals (Sweden)
Alhaji Jibrilla Aliyu
2015-12-01
Full Text Available This paper empirically examines the long-run pass through of the official exchange rates into trade balance in Nigeria by means of threshold cointegration and asymmetric error correction modeling. The study provides evidence for non-linear cointegration between our variables of interest. The estimated asymmetric error correction models provide new evidence for slower transmission of exchange rate depreciations into the country’s trade balance, which in turn appears to offer partial support for the Dutch disease hypothesis. This finding suggests that policy-makers cannot hope to use currency devaluation to improve the trade balance. It is recommended that policy-makers focus attention on diversification of the economy away from dependence on crude oil exports into productive manufacturing and non-oil exports, which will be vital in making the economy more competitive.
Testing and inference in nonlinear cointegrating vector error correction models
DEFF Research Database (Denmark)
Kristensen, D.; Rahbek, A.
2013-01-01
We analyze estimators and tests for a general class of vector error correction models that allows for asymmetric and nonlinear error correction. For a given number of cointegration relationships, general hypothesis testing is considered, where testing for linearity is of particular interest. Under...... the null of linearity, parameters of nonlinear components vanish, leading to a nonstandard testing problem. We apply so-called sup-tests to resolve this issue, which requires development of new(uniform) functional central limit theory and results for convergence of stochastic integrals. We provide a full...... asymptotic theory for estimators and test statistics. The derived asymptotic results prove to be nonstandard compared to results found elsewhere in the literature due to the impact of the estimated cointegration relations. This complicates implementation of tests motivating the introduction of bootstrap...
Pairs Trading to the Commodities Futures Market Using Cointegration Method
Directory of Open Access Journals (Sweden)
Cüneyt Ungever
2015-10-01
Full Text Available This paper investigates pairs trading strategy by using the cointegration method among the 10 most popular agricultural future markets. It is found that only in 2 pairs shows trading signal. The pairs trading strategy is performed in two stages that are the formation period and the trading period with daily futures data from 2004 to 2015. After the formation period was constructed, it is assumed that the cointegration error continues to hold the trading period same as it does for the formation period. The pairs trading strategy is created by the long position cotton and the short position coffee and also long position cotton and short position the livecattle. It is found that the profitability of this strategy worked well in both formation period and trading period.
A contingency table approach to nonparametric testing
Rayner, JCW
2000-01-01
Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp
Exchange Rate – Relative Price Nonlinear Cointegration Relationship in Malaysia
Venus Khim-Sen Liew; Chee-Keong Choong; Evan Lau; Kian-Ping Lim
2005-01-01
The finding of exchange rate–relative price nonlinear cointegration relationship in Malaysia, among others, suggests that nonlinear Purchasing Power Parity (PPP) equilibrium may be regarded as reference point in judging the short run misalignment of the Ringgit currency and thereby deducing effective policy actions. Moreover, economists who wish to extend the simple PPP exchange rate model into the more complicated monetary exchange models may do so comfortably, at least in the text of Malays...
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....
Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support
Czech Academy of Sciences Publication Activity Database
Kalina, Jan; Zvárová, Jana
2017-01-01
Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability
Non-parametric tests of productive efficiency with errors-in-variables
Kuosmanen, T.K.; Post, T.; Scholtes, S.
2007-01-01
We develop a non-parametric test of productive efficiency that accounts for errors-in-variables, following the approach of Varian. [1985. Nonparametric analysis of optimizing behavior with measurement error. Journal of Econometrics 30(1/2), 445-458]. The test is based on the general Pareto-Koopmans
DEFF Research Database (Denmark)
Tabor, Morten Nyboe
2014-01-01
conrm the original intuition behind the attempt to combine IKE and the econometric approach based on the cointegrated VAR model, that the parameter-instability of IKE models could potentially be an important source of the persistence found empirically in estimated cointegrated VAR models...
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
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...
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....
Exchange Rate Volatility and Investment: A Panel Data Cointegration Approach
Directory of Open Access Journals (Sweden)
Ibrahima Amadou DIALLO
2015-05-01
Full Text Available This paper examines the link between real exchange rate volatility and domestic investment by using panel data cointegration techniques. We study the empirical connection between real effective exchange rate volatility and investment for 51 developing countries (23 low-income and 28 middle-income countries. The theoretical relationship between investment and real exchange rate volatility predicts that the effects of exchange rate uncertainty on profits are ambiguous. The empirical results illustrate that real effective exchange rate volatility has a strong negative impact on investment. This outcome is robust in low income and middle income countries, and by using an alternative measurement of exchange rate volatility.
Nonparametric methods in actigraphy: An update
Directory of Open Access Journals (Sweden)
Bruno S.B. Gonçalves
2014-09-01
Full Text Available Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm results for each time interval. Simulated data showed that (1 synchronization analysis depends on sample size, and (2 fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization.
Within and Between Panel Cointegration in the German Regional Output-Trade-FDI Nexus
DEFF Research Database (Denmark)
Mitze, Timo
For spatial data with a sufficiently long time dimension, the concept of global cointegration has been recently included in the econometrics research agenda. Global cointegration arises when non-stationary time series are cointegrated both within and between spatial units. In this paper, we analyze...... the role of globally cointegrated variable relationships using German regional data (NUTS 1 level) for GDP, trade, and FDI activity during the period 1976–2005. Applying various homogeneous and heterogeneous panel data estimators to a Spatial Panel Error Correction Model (SpECM) for regional output growth...... allows us to analyze the short- and long-run impacts of internationalization activities. For the long-run cointegration equation, the empirical results support the hypothesis of export- and FDI-led growth. We also show that for export and outward FDI activity positive cross-regional eff ects are at work...
Nonparametric functional mapping of quantitative trait loci.
Yang, Jie; Wu, Rongling; Casella, George
2009-03-01
Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.
Essays on nonparametric econometrics of stochastic volatility
Zu, Y.
2012-01-01
Volatility is a concept that describes the variation of financial returns. Measuring and modelling volatility dynamics is an important aspect of financial econometrics. This thesis is concerned with nonparametric approaches to volatility measurement and volatility model validation.
Nonparametric methods for volatility density estimation
Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.
2009-01-01
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on
Cointegration and the demand for gasoline
International Nuclear Information System (INIS)
Bhaskara Rao, B.; Rao, Gyaneshwar
2009-01-01
Since the early 1970s, there has been a worldwide upsurge in the price of energy and in particular of gasoline. Therefore, demand functions for energy and its components like gasoline have received much attention. However, since confidence in the estimated demand functions is important for use in policy and forecasting, following [Amarawickrama, H.A., Hunt, L.C., 2008. Electricity demand for Sri Lanka: A time series analysis. Energy Economics 33, 724-739], this paper estimates the demand for gasoline is estimated with five alternative time series techniques with data from Fiji. Estimates with these alternative techniques are very close, and thus increase our confidence in them. We found that gasoline demand is both price and income inelastic.
Cointegration and the demand for gasoline
Energy Technology Data Exchange (ETDEWEB)
Bhaskara Rao, B. [University of Western Sydney, Sydney1797 (Australia); Rao, Gyaneshwar [University of the South Pacific (Fiji)
2009-10-15
Since the early 1970s, there has been a worldwide upsurge in the price of energy and in particular of gasoline. Therefore, demand functions for energy and its components like gasoline have received much attention. However, since confidence in the estimated demand functions is important for use in policy and forecasting, following [Amarawickrama, H.A., Hunt, L.C., 2008. Electricity demand for Sri Lanka: A time series analysis. Energy Economics 33, 724-739], this paper estimates the demand for gasoline is estimated with five alternative time series techniques with data from Fiji. Estimates with these alternative techniques are very close, and thus increase our confidence in them. We found that gasoline demand is both price and income inelastic. (author)
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...
An estimation of U.S. gasoline demand. A smooth time-varying cointegration approach
International Nuclear Information System (INIS)
Park, Sung Y.; Zhao, Guochang
2010-01-01
In this paper the U.S. gasoline demand from 1976 to 2008 is estimated using a time-varying cointegrating regression. We find that price elasticity increased rapidly during the late 1970s and then decreased until 1987. After a relatively small-scaled 'increase-decrease' cycle from 1987 to 2000, the price elasticity rose again after 2000. The time-varying change of the elasticities may be explained by the proportion of gasoline consumption to income and fluctuation of the degree of necessity. The result of the error correction model shows that a deviation from a long-run equilibrium is corrected quickly, and the welfare analysis illustrates there may be a gain by shifting the tax scheme from income tax to gasoline tax. (author)
An estimation of U.S. gasoline demand. A smooth time-varying cointegration approach
Energy Technology Data Exchange (ETDEWEB)
Park, Sung Y. [Department of Economics, University of Illinois, Urbana, IL 61801 (United States); The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005 (China); Zhao, Guochang [Research School of Economics, College of Business and Economics, The Australian National University, Canberra, ACT 2601 (Australia)
2010-01-15
In this paper the U.S. gasoline demand from 1976 to 2008 is estimated using a time-varying cointegrating regression. We find that price elasticity increased rapidly during the late 1970s and then decreased until 1987. After a relatively small-scaled 'increase-decrease' cycle from 1987 to 2000, the price elasticity rose again after 2000. The time-varying change of the elasticities may be explained by the proportion of gasoline consumption to income and fluctuation of the degree of necessity. The result of the error correction model shows that a deviation from a long-run equilibrium is corrected quickly, and the welfare analysis illustrates there may be a gain by shifting the tax scheme from income tax to gasoline tax. (author)
Single versus mixture Weibull distributions for nonparametric satellite reliability
International Nuclear Information System (INIS)
Castet, Jean-Francois; Saleh, Joseph H.
2010-01-01
Long recognized as a critical design attribute for space systems, satellite reliability has not yet received the proper attention as limited on-orbit failure data and statistical analyses can be found in the technical literature. To fill this gap, we recently conducted a nonparametric analysis of satellite reliability for 1584 Earth-orbiting satellites launched between January 1990 and October 2008. In this paper, we provide an advanced parametric fit, based on mixture of Weibull distributions, and compare it with the single Weibull distribution model obtained with the Maximum Likelihood Estimation (MLE) method. We demonstrate that both parametric fits are good approximations of the nonparametric satellite reliability, but that the mixture Weibull distribution provides significant accuracy in capturing all the failure trends in the failure data, as evidenced by the analysis of the residuals and their quasi-normal dispersion.
The role of cointegration for optimal hedging with heteroscedastic error term
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
2017-01-01
a cointegrating vector for large h, thereby giving a bounded risk. Taking the expected return into account, the portfolio that maximizes the Sharpe ratio is found, and it is shown that it also approaches a cointegration portfolio. For constant conditional volatility, the conditional variance can be estimated...... forward contracts for electricity, which are hedged by forward contracts for fuel prices. The main conclusion of the paper is that for optimal hedging, one should exploit the cointegrating properties for long horizons, but for short horizons more weight should be put on the remaining dynamics....
The role of cointegration for optimal hedging with heteroscedastic error term
DEFF Research Database (Denmark)
Gatarek, Lutasz; Johansen, Søren
a cointegrating vector for large h, thereby giving a bounded risk. Taking the expected return into account, the portfolio that maximizes the Sharpe ratio is found, and it is shown that it also approaches a cointegration portfolio. For constant conditional volatility, the conditional variance can be estimated...... forward contracts for electricity, which are hedged by forward contracts for fuel prices. The main conclusion of the paper is that for optimal hedging, one should exploit the cointegrating properties for long horizons, but for short horizons more weight should be put on the remaining dynamics....
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....
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....
Co-integration Rank Testing under Conditional Heteroskedasticity
DEFF Research Database (Denmark)
Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert
null distributions of the rank statistics coincide with those derived by previous authors who assume either i.i.d. or (strict and covariance) stationary martingale difference innovations. We then propose wild bootstrap implementations of the co-integrating rank tests and demonstrate that the associated...... bootstrap rank statistics replicate the first-order asymptotic null distributions of the rank statistics. We show the same is also true of the corresponding rank tests based on the i.i.d. bootstrap of Swensen (2006). The wild bootstrap, however, has the important property that, unlike the i.i.d. bootstrap......, it preserves in the re-sampled data the pattern of heteroskedasticity present in the original shocks. Consistent with this, numerical evidence sug- gests that, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples un...
Recent Advances and Trends in Nonparametric Statistics
Akritas, MG
2003-01-01
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection o
Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices
Hiroyuki Kasahara; Katsumi Shimotsu
2006-01-01
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...
Aristovnik, Aleksander
2012-01-01
The purpose of the paper is to review some previous researches examining ICT efficiency and the impact of ICT on educational output/outcome as well as different conceptual and methodological issues related to performance measurement. Moreover, a definition, measurements and the empirical application of a model measuring the efficiency of ICT use and its impact at national levels will be considered. For this purpose, the Data Envelopment Analysis (DEA) technique is presented and then applied t...
Causality between government revenue and expenditure in Malaysia: A seasonal cointegration test
Goh, Soo Khoon; Dawood, Mithani
1999-01-01
The objective of this article is to empirically incorporate the effect of seasonality in examining the causal relationship between quarterly government revenue and governemnt expenditure in Malaysia for the period 1970.1- 1994.4. The seasonal integration and cointegration tests developed by Hylleberg, Engle, Granger and Yoo (1990) and extended by Engle, Granger, Hylleberg and lee (1993) are applied prior to determination of causality. Evidence of seasonal cointegration of biaanual frequency ...
Do Markets Cointegrate after Financial Crises? Evidence from G-20 Stock Markets
Directory of Open Access Journals (Sweden)
Mahfuzul Haque
2015-12-01
Full Text Available The results of the single-equation cointegration tests indicate that patterns of cointegration in the two main and four sub-periods are not homogeneous. Two key findings emerge from the study. First, fewer stock markets cointegrated with S&P 500 during the crisis period than they did during the pre-crisis. In other words, as the 2008 financial crisis deepened, S&P 500 and G-20 stock indices moved towards less cointegration. The decreasing number of cointegrating relationships implies that the U.S. stock markets and other G-20 markets have experienced different driving forces since the start of the U.S. crisis. Second, among those markets that are cointegrated with S&P 500, they happened to be deeply affected by S&P and the shocks emerging from it. The 2007–2009 financial crises can be considered a structural break in the long-run relationship and may have resulted from effective joint intervention/responses taken by members of G-20 nations.
Shi, Haichen; Worden, Keith; Cross, Elizabeth J.
2018-03-01
Cointegration is now extensively used to model the long term common trends among economic variables in the field of econometrics. Recently, cointegration has been successfully implemented in the context of structural health monitoring (SHM), where it has been used to remove the confounding influences of environmental and operational variations (EOVs) that can often mask the signature of structural damage. However, restrained by its linear nature, the conventional cointegration approach has limited power in modelling systems where measurands are nonlinearly related; this occurs, for example, in the benchmark study of the Z24 Bridge, where nonlinear relationships between natural frequencies were induced during a period of very cold temperatures. To allow the removal of EOVs from SHM data with nonlinear relationships like this, this paper extends the well-established cointegration method to a nonlinear context, which is to allow a breakpoint in the cointegrating vector. In a novel approach, the augmented Dickey-Fuller (ADF) statistic is used to find which position is most appropriate for inserting a breakpoint, the Johansen procedure is then utilised for the estimation of cointegrating vectors. The proposed approach is examined with a simulated case and real SHM data from the Z24 Bridge, demonstrating that the EOVs can be neatly eliminated.
Teaching Nonparametric Statistics Using Student Instrumental Values.
Anderson, Jonathan W.; Diddams, Margaret
Nonparametric statistics are often difficult to teach in introduction to statistics courses because of the lack of real-world examples. This study demonstrated how teachers can use differences in the rankings and ratings of undergraduate and graduate values to discuss: (1) ipsative and normative scaling; (2) uses of the Mann-Whitney U-test; and…
Nonparametric conditional predictive regions for time series
de Gooijer, J.G.; Zerom Godefay, D.
2000-01-01
Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2004-01-01
Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric
Non-Parametric Estimation of Correlation Functions
DEFF Research Database (Denmark)
Brincker, Rune; Rytter, Anders; Krenk, Steen
In this paper three methods of non-parametric correlation function estimation are reviewed and evaluated: the direct method, estimation by the Fast Fourier Transform and finally estimation by the Random Decrement technique. The basic ideas of the techniques are reviewed, sources of bias are point...
Nonparametric estimation in models for unobservable heterogeneity
Hohmann, Daniel
2014-01-01
Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.; Lombard, F.
2012-01-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Li, Qi-Gang; He, Yong-Han; Wu, Huan; Yang, Cui-Ping; Pu, Shao-Yan; Fan, Song-Qing; Jiang, Li-Ping; Shen, Qiu-Shuo; Wang, Xiao-Xiong; Chen, Xiao-Qiong; Yu, Qin; Li, Ying; Sun, Chang; Wang, Xiangting; Zhou, Jumin; Li, Hai-Peng; Chen, Yong-Bin; Kong, Qing-Peng
2017-01-01
Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption-both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo , to be crucial in tumorigenesis, e.g., alcohol metabolism ( ADH1B ), chromosome remodeling ( NCAPH ) and complement system ( Adipsin ). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.
Directory of Open Access Journals (Sweden)
Taeyoung Jin
2018-03-01
Full Text Available This paper examines the relationship between coal consumption and economic growth for 30 OECD (Organisation for Economic Co-operation and Development countries and 32 non-OECD countries for 1990–2013 using a multivariate dependent panel analysis. For the analysis, we conducted the common factor defactorization process, unit root test, cointegration test, long-run cointegrating vector, and Granger causality test. Our results suggest the following: First, there is no long-run relationship between coal consumption and economic growth in OECD countries; however, in non-OECD countries, the relationship does exist. Second, excessive coal usage may hinder economic growth in the long run. Lastly, the growth hypothesis (coal consumption affects economic growth positively is supported in the short run for non-OECD countries. As coal consumption has a positive effect on economic growth in the short run and a negative effect in the long run, energy conservation policies may have adverse effects only in the short run. Thus, non-OECD countries should gradually switch their energy mix to become less coal-dependent as they consider climate change. Moreover, a transfer of technology and financial resources from developed to developing countries must be encouraged at a global level.
Directory of Open Access Journals (Sweden)
Corrado lo Storto
2013-12-01
Full Text Available This article reports the outcome of a performance study of the water service provision industry in Italy. The study evaluates the efficiency of 21 “private or public-private” equity and 32 “public” equity water service operators and investigates controlling factors. In particular, the influence that the operator typology and service management nature - private vs. public - has on efficiency is assessed. The study employed a two-stage Data Envelopment Analysis methodology. In the first stage, the operational efficiency of water supply operators is calculated by implementing a conventional BCC DEA model, that uses both physical infrastructure and financial input and output variables to explore economies of scale. In the second stage, bootstrapped DEA and Tobit regression are performed to estimate the influence that a number of environmental factors have on water supplier efficiency. The results show that the integrated water provision industry in Italy is characterized by operational inefficiencies of service operators, and scale and agglomeration economies may have a not negligible effect on efficiency. In addition, the operator typology and its geographical location affect efficiency.
Nonparametric Change Point Diagnosis Method of Concrete Dam Crack Behavior Abnormality
Directory of Open Access Journals (Sweden)
Zhanchao Li
2013-01-01
Full Text Available The study on diagnosis method of concrete crack behavior abnormality has always been a hot spot and difficulty in the safety monitoring field of hydraulic structure. Based on the performance of concrete dam crack behavior abnormality in parametric statistical model and nonparametric statistical model, the internal relation between concrete dam crack behavior abnormality and statistical change point theory is deeply analyzed from the model structure instability of parametric statistical model and change of sequence distribution law of nonparametric statistical model. On this basis, through the reduction of change point problem, the establishment of basic nonparametric change point model, and asymptotic analysis on test method of basic change point problem, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is created in consideration of the situation that in practice concrete dam crack behavior may have more abnormality points. And the nonparametric change point diagnosis method of concrete dam crack behavior abnormality is used in the actual project, demonstrating the effectiveness and scientific reasonableness of the method established. Meanwhile, the nonparametric change point diagnosis method of concrete dam crack behavior abnormality has a complete theoretical basis and strong practicality with a broad application prospect in actual project.
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...
Cointegration and Causality Test Among Export, Import, and Foreign Exchange
Directory of Open Access Journals (Sweden)
Haryono Subiyakto
2016-06-01
Full Text Available The rupiah exchange rate, import, and export are the important indicators in economy, including the Indonesia economy. The debate regarding the relationship among the exchange rate, import, and export has been persisting for several decades. Some researchers found that there is a relationship among those three and others explained that there is no correlation among them. The aim of this research is to obtain the empirical evidence of the causal relationship among the export, import, and foreign exchange rate by using the monthly data from January 2010 to April 2014. The export and import data are the export and import values in US dollar. The exchange rate data is the median exchange rates of the Indonesian Bank. The Johansen Cointegration Test and the Granger Causality Test are used to analyze the data. The research result shows that export and import have no causal relationship at five percent. Next, the foreign exchange rate influences the export and import at 10 percent level. The result indicates that the foreign exchange rate has small effects on the export and import. Based on the results, the government should control the balance of trade and should not make any policy that is based on the exchange rate values. Finally, it can be said that the exchange rate policy is not effective in increasing the exports and reducing the imports.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...... considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Modeling new product sales; an application of cointegration analysis
Ph.H.B.F. Franses (Philip Hans)
1994-01-01
textabstractThis paper considers an econometric model for new product sales, which extends the deterministic Gompertz process by allowing the saturation level to depend on forcing variables. It turns out that this model can be written in error correction form. Given that many marketing variables
Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm
Huang, Junbing; Tang, Yuee; Chen, Shuxing
2018-01-01
Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to ...
A cointegration analysis of Danish zero-coupon bond yields
DEFF Research Database (Denmark)
Engsted, Tom; Tanggaard, Carsten
1994-01-01
Der præsenteres en ny database af danske nul-kupon obligationsrenter for perioden 1976-1991, og forventningshypotesens kointegrationsimplikationer testes v.h.a. multivariate kointegrationsmetoder. Udgivelsesdato: August...
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
portfolio optimization based on nonparametric estimation methods
Directory of Open Access Journals (Sweden)
mahsa ghandehari
2017-03-01
Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.
Nonparametric Mixture Models for Supervised Image Parcellation.
Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina
2009-09-01
We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.
Robustifying Bayesian nonparametric mixtures for count data.
Canale, Antonio; Prünster, Igor
2017-03-01
Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.
Schmith, Torben; Thejll, Peter; Johansen, Søren
2016-04-01
We analyse the statistical relationship between changes in global temperature, global steric sea level and radiative forcing in order to reveal causal relationships. There are in this, however, potential pitfalls due to the trending nature of the time series. We therefore apply a statistical method called cointegration analysis, originating from the field of econometrics, which is able to correctly handle the analysis of series with trends and other long-range dependencies. Further, we find a relationship between steric sea level and temperature and find that temperature causally depends on the steric sea level, which can be understood as a consequence of the large heat capacity of the ocean. This result is obtained both when analyzing observed data and data from a CMIP5 historical model run. Finally, we find that in the data from the historical run, the steric sea level, in turn, is driven by the external forcing. Finally, we demonstrate that combining these two results can lead to a novel estimate of radiative forcing back in time based on observations.
Income and CO2 emissions: Evidence from panel unit root and cointegration tests
International Nuclear Information System (INIS)
Lee, C.-C.; Lee, J.-D.
2009-01-01
This paper re-investigates the stationarity properties of per capita carbon dioxide (CO 2 ) emissions and real Gross Domestic Product (GDP) per capita for 109 countries within seven regional panel sets covering 1971-2003. We apply the recent unit-root test of the panel seemingly unrelated regressions augmented Dickey-Fuller (SURADF) test developed by Breuer et al. [2001. Misleading inferences from panel unit-root tests with an illustration from purchasing power parity. Review of International Economics 9, 482-493; 2002. Series-specific unit-root tests with panel data, Oxford Bulletin of Economics and Statistics 64, 527-546]. The panel SURADF test accounts for the presence of cross-country correlations in the data, and the parameters in the panel specification vary across countries. More importantly, this test allows us to identify how many and which members of the panel contain a unit root. Overall, our empirical results illustrate that real GDP and CO 2 emissions in these countries are a mixture of I(0) and I(1) processes, and that the traditional panel unit-root tests could lead to misleading inferences as well as the conduct of cointegration analysis being perhaps inappropriate. The results of our analysis carry critical implications for the modeling of CO 2 emissions and GDP because of the different orders of integration for the two variables
Cointegration as a data normalization tool for structural health monitoring applications
Harvey, Dustin Y.; Todd, Michael D.
2012-04-01
The structural health monitoring literature has shown an abundance of features sensitive to various types of damage in laboratory tests. However, robust feature extraction in the presence of varying operational and environmental conditions has proven to be one of the largest obstacles in the development of practical structural health monitoring systems. Cointegration, a technique adapted from the field of econometrics, has recently been introduced to the SHM field as one solution to the data normalization problem. Response measurements and feature histories often show long-run nonstationarity due to fluctuating temperature, load conditions, or other factors that leads to the occurrence of false positives. Cointegration theory allows nonstationary trends common to two or more time series to be modeled and subsequently removed. Thus, the residual retains sensitivity to damage with dependence on operational and environmental variability removed. This study further explores the use of cointegration as a data normalization tool for structural health monitoring applications.
Micromachined Thin-Film Sensors for SOI-CMOS Co-Integration
Laconte, Jean; Flandre, D.; Raskin, Jean-Pierre
Co-integration of sensors with their associated electronics on a single silicon chip may provide many significant benefits regarding performance, reliability, miniaturization and process simplicity without significantly increasing the total cost. Micromachined Thin-Film Sensors for SOI-CMOS Co-integration covers the challenges and interests and demonstrates the successful co-integration of gas flow sensors on dielectric membrane, with their associated electronics, in CMOS-SOI technology. We firstly investigate the extraction of residual stress in thin layers and in their stacking and the release, in post-processing, of a 1 μm-thick robust and flat dielectric multilayered membrane using Tetramethyl Ammonium Hydroxide (TMAH) silicon micromachining solution.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
Zhou, Wei-Xing; Sornette, Didier
We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.
Bootstrap Sequential Determination of the Co-integration Rank in VAR Models
DEFF Research Database (Denmark)
Guiseppe, Cavaliere; Rahbæk, Anders; Taylor, A.M. Robert
with empirical rejection frequencies often very much in excess of the nominal level. As a consequence, bootstrap versions of these tests have been developed. To be useful, however, sequential procedures for determining the co-integrating rank based on these bootstrap tests need to be consistent, in the sense...... in the literature by proposing a bootstrap sequential algorithm which we demonstrate delivers consistent cointegration rank estimation for general I(1) processes. Finite sample Monte Carlo simulations show the proposed procedure performs well in practice....
A Nonparametric Test for Seasonal Unit Roots
Kunst, Robert M.
2009-01-01
Abstract: We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order to cope with the sensitivity of the original RUR test to autocorrelation under its null of a unit root, we suggest an augmentation step by autoregression. We present some evidence on the siz...
A Bayesian nonparametric estimation of distributions and quantiles
International Nuclear Information System (INIS)
Poern, K.
1988-11-01
The report describes a Bayesian, nonparametric method for the estimation of a distribution function and its quantiles. The method, presupposing random sampling, is nonparametric, so the user has to specify a prior distribution on a space of distributions (and not on a parameter space). In the current application, where the method is used to estimate the uncertainty of a parametric calculational model, the Dirichlet prior distribution is to a large extent determined by the first batch of Monte Carlo-realizations. In this case the results of the estimation technique is very similar to the conventional empirical distribution function. The resulting posterior distribution is also Dirichlet, and thus facilitates the determination of probability (confidence) intervals at any given point in the space of interest. Another advantage is that also the posterior distribution of a specified quantitle can be derived and utilized to determine a probability interval for that quantile. The method was devised for use in the PROPER code package for uncertainty and sensitivity analysis. (orig.)
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing
DEFF Research Database (Denmark)
Hald, Ditte Høvenhoff
The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...... instantaneous and convolutive mixing, and the inferred temporal patterns. Spatial maps are seen to capture smooth and localized stimuli-related components, and often identifiable noise components. The implementation is freely available as a GUI/SPM plugin, and we recommend using GPICA as an additional tool when...
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
Nonparametric Bayesian density estimation on manifolds with applications to planar shapes.
Bhattacharya, Abhishek; Dunson, David B
2010-12-01
Statistical analysis on landmark-based shape spaces has diverse applications in morphometrics, medical diagnostics, machine vision and other areas. These shape spaces are non-Euclidean quotient manifolds. To conduct nonparametric inferences, one may define notions of centre and spread on this manifold and work with their estimates. However, it is useful to consider full likelihood-based methods, which allow nonparametric estimation of the probability density. This article proposes a broad class of mixture models constructed using suitable kernels on a general compact metric space and then on the planar shape space in particular. Following a Bayesian approach with a nonparametric prior on the mixing distribution, conditions are obtained under which the Kullback-Leibler property holds, implying large support and weak posterior consistency. Gibbs sampling methods are developed for posterior computation, and the methods are applied to problems in density estimation and classification with shape-based predictors. Simulation studies show improved estimation performance relative to existing approaches.
Directory of Open Access Journals (Sweden)
Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Comparison between goal programming and cointegration approaches in enhanced index tracking
Lam, Weng Siew; Jamaan, Saiful Hafizah Hj.
2013-04-01
Index tracking is a popular form of passive fund management in stock market. Passive management is a buy-and-hold strategy that aims to achieve rate of return similar to the market return. Index tracking problem is a problem of reproducing the performance of a stock market index, without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio that minimizes risk or tracking error. An improved index tracking (enhanced index tracking) is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the tracking error. Enhanced index tracking aims to generate excess return over the return achieved by the index. The objective of this study is to compare the portfolio compositions and performances by using two different approaches in enhanced index tracking problem, which are goal programming and cointegration. The result of this study shows that the optimal portfolios for both approaches are able to outperform the Malaysia market index which is Kuala Lumpur Composite Index. Both approaches give different optimal portfolio compositions. Besides, the cointegration approach outperforms the goal programming approach because the cointegration approach gives higher mean return and lower risk or tracking error. Therefore, the cointegration approach is more appropriate for the investors in Malaysia.
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Carlini, Federico; Santucci de Magistris, Paolo
with different fractional integration and cointegration parameters. The properties of these multiple non-identified sub-models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d). The assessment of the F(d...
A cointegration approach to forecasting freight rates in the dry bulk shipping sector
Ph.H.B.F. Franses (Philip Hans); A.W. Veenstra (Albert)
1997-01-01
textabstractIn this paper, a vector autoregressive model is developed for a sample of ocean dry bulk freight rates. Although the series of freight rates are themselves found to be non-stationary, thus precluding the use of many modelling methodologies, evidence provided by cointegration tests points
The Effect of Foreign Aid on Income Inequality: Evidence from Panel Cointegration
Dierk Herzer, Peter Nunnenkamp
2012-01-01
This paper examines the long-run effect of foreign aid on income inequality for 21 recipient countries using panel cointegration techniques to control for omitted variable and endogeneity bias. We find that aid exerts an inequality increasing effect on income distribution
Stock Market Optimism and Cointegration among Stocks: The Case of the Prague Stock Exchange
Czech Academy of Sciences Publication Activity Database
Baxa, Jaromír
2007-01-01
Roč. 15, č. 4 (2007), s. 5-16 ISSN 0572-3043 R&D Projects: GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : stock market * optimism * cointegration Subject RIV: AH - Economics
Malthus in cointegration space: evidence of a post-Malthusian pre-industrial England
DEFF Research Database (Denmark)
Møller, Niels Framroze; Sharp, Paul
2014-01-01
income per capita continued to spur population growth but was no longer stagnant. Our formulation of a post-Malthusian hypothesis implies cointegration between vital rates (birth- and death rates) and income and builds explicitly on a simple model of Malthusian stagnation. We show that this hypothesis...
Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models
Hallin, M.; van den Akker, R.; Werker, B.J.M.
2012-01-01
Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the
A No-Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges
DEFF Research Database (Denmark)
Rossi, Eduardo; Santucci de Magistris, Paolo
2013-01-01
The no-arbitrage relation between futures and spot prices implies an analogous relation between futures and spot daily ranges. The long-memory features of the range-based volatility estimators are analyzed, and fractional cointegration is tested in a semi-parametric framework. In particular, the no...
Directory of Open Access Journals (Sweden)
Tara Ninta Ikrima
2015-06-01
Full Text Available Penelitian ini bertujuan untuk menganalisis dampak krisis di Yunani terhadap pergerakan harga saham syariah di Indonesia, Malaysia, Amerika Serikat, dan Eropa. Selain itu, penelitian ini juga menganalisis co-integrasi dan efek penularan (contagion effect yang terjadi selama periode ini. Penelitian ini dilakukan karena ada perbedaan antara hasil penelitian tentang US Subprime Mortgage periode krisis tentang dampak pasar saham syariah. Penelitian ini menggunakan VAR (Vector Auto Regressive dan VECM (Vector Error Correction Model untuk menguji hipotesis dengan EViews 6 digunakan sebagai alat analisis statistik. Data yang digunakan dalam penelitian ini adalah indeks harga saham penutupan mingguan yang diambil dari perwakilan pasar saham syariah masing-masing negara, JII untuk Indonesia, DJIMY untuk Malaysia, DJIM US, dan MSCI untuk Eropa. Hasilnya menunjukkan bahwa Krisis Yunani tidak memiliki pengaruh terhadap pergerakan harga saham Islam di AS, Malaysia, Indonesia, dan Eropa. Namun ada co-integrasi dan penularan berpengaruh terhadap harga saham Islam di empat wilayah saat krisis Yunani itu terjadi. The objective of the study was to analyze the Greece’s crisis impacts toward the movement of Islamic stock prices in Indonesia, Malaysia, USA, and Europe. Moreover, this study also analyzed co-integration and contagion effect which occurred during the period. VAR (Vector Auto Regressive and VECM (Vector Error Correction Model with eviews 6 were used to test the hypothesis as the statistical analysis tools. The data of this study were the weekly closing stock price indices taken from the representatives of Islamic stock markets of each country; JII in Indonesia, DJIMY in Malaysia, DJIM in USA, and MSCI in Europe. The result showed that the Greece’s crisis did not give any influence toward the movement of Islamic stock prices in USA, Malaysia, Indonesia, and Europe. However; there were co-integration and contagion effect which influenced on Islamic
On Parametric (and Non-Parametric Variation
Directory of Open Access Journals (Sweden)
Neil Smith
2009-11-01
Full Text Available This article raises the issue of the correct characterization of ‘Parametric Variation’ in syntax and phonology. After specifying their theoretical commitments, the authors outline the relevant parts of the Principles–and–Parameters framework, and draw a three-way distinction among Universal Principles, Parameters, and Accidents. The core of the contribution then consists of an attempt to provide identity criteria for parametric, as opposed to non-parametric, variation. Parametric choices must be antecedently known, and it is suggested that they must also satisfy seven individually necessary and jointly sufficient criteria. These are that they be cognitively represented, systematic, dependent on the input, deterministic, discrete, mutually exclusive, and irreversible.
Nonparametric predictive pairwise comparison with competing risks
International Nuclear Information System (INIS)
Coolen-Maturi, Tahani
2014-01-01
In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Nonparametric Estimation of Distributions in Random Effects Models
Hart, Jeffrey D.
2011-01-01
We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.
Spurious Seasonality Detection: A Non-Parametric Test Proposal
Directory of Open Access Journals (Sweden)
Aurelio F. Bariviera
2018-01-01
Full Text Available This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.
Nonparametric estimation of stochastic differential equations with sparse Gaussian processes.
García, Constantino A; Otero, Abraham; Félix, Paulo; Presedo, Jesús; Márquez, David G
2017-08-01
The application of stochastic differential equations (SDEs) to the analysis of temporal data has attracted increasing attention, due to their ability to describe complex dynamics with physically interpretable equations. In this paper, we introduce a nonparametric method for estimating the drift and diffusion terms of SDEs from a densely observed discrete time series. The use of Gaussian processes as priors permits working directly in a function-space view and thus the inference takes place directly in this space. To cope with the computational complexity that requires the use of Gaussian processes, a sparse Gaussian process approximation is provided. This approximation permits the efficient computation of predictions for the drift and diffusion terms by using a distribution over a small subset of pseudosamples. The proposed method has been validated using both simulated data and real data from economy and paleoclimatology. The application of the method to real data demonstrates its ability to capture the behavior of complex systems.
Nonparametric estimation of benchmark doses in environmental risk assessment
Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen
2013-01-01
Summary An important statistical objective in environmental risk analysis is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose-response experiment. In such settings, representations of the risk are traditionally based on a parametric dose-response model. It is a well-known concern, however, that if the chosen parametric form is misspecified, inaccurate and possibly unsafe low-dose inferences can result. We apply a nonparametric approach for calculating benchmark doses, based on an isotonic regression method for dose-response estimation with quantal-response data (Bhattacharya and Kong, 2007). We determine the large-sample properties of the estimator, develop bootstrap-based confidence limits on the BMDs, and explore the confidence limits’ small-sample properties via a short simulation study. An example from cancer risk assessment illustrates the calculations. PMID:23914133
A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING
Temel, Tugrul T.
2001-01-01
This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.
Simple nonparametric checks for model data fit in CAT
Meijer, R.R.
2005-01-01
In this paper, the usefulness of several nonparametric checks is discussed in a computerized adaptive testing (CAT) context. Although there is no tradition of nonparametric scalability in CAT, it can be argued that scalability checks can be useful to investigate, for example, the quality of item
Nonparametric Bayesian inference for multidimensional compound Poisson processes
Gugushvili, S.; van der Meulen, F.; Spreij, P.
2015-01-01
Given a sample from a discretely observed multidimensional compound Poisson process, we study the problem of nonparametric estimation of its jump size density r0 and intensity λ0. We take a nonparametric Bayesian approach to the problem and determine posterior contraction rates in this context,
Energy Technology Data Exchange (ETDEWEB)
Engsted, T; Bentzen, J
1997-04-01
This paper provides a detailed survey of the recent literature on unit roots and co-integration, and relates the concepts to the estimation of energy demand relationships. The special features and properties of non-stationary time-series are discussed, including the relevant asymptotic theory. The most often used tests for unit roots and co-integration - and various techniques for estimating co-integration relationships - are described, and the connection between co-integration and error-correction models is explored. Further, we revisit the autoregressive distributed lag (ADL) model, which is very often used in energy demand studies, and state under which conditions this model provides a valid framework for estimating income- and price- elasticities, when time-series are non-stationary. Throughout, tests and estimation techniques are illustrated using data on Danish energy consumption, prices, income, and temperature. (au) 71 refs.
Fukuda, Takashi; Dahalan, Jauhari
2011-01-01
This paper attempts to explore a new dimension of India’s ‘financegrowth- crisis’ nexus. For this end, the summary indicators of financial development, financial crisis and financial repression are created through the principal component approach, and we perform the cointegration and Granger causality analysis employing the methods of vector error correction model (VECM) and autoregressive distributed lag (ARDL). The element of structural break is also taken into assessment while specifying t...
Shahbaz, Muhammad; Khraief, Naceur; Dhaoui, Abderrazak
2015-01-01
This paper investigates the causal relationship between road transportation energy consumption, fuel prices, transport sector value added and CO2 emissions in Tunisia for the period 1980-2012. We apply the newly developed combined cointegration test proposed by Bayer and Hanck (2013) and the ARDL bounds testing approach to cointegration to establish the existence of long-run relationship in presence of structural breaks. The direction of causality between these variables is determined via vec...
International Nuclear Information System (INIS)
Glasure, Yong U.; Lee, Aie-Rie
1998-01-01
This paper examines the causality issue between energy consumption and GDP for South Korea and Singapore, with the aid of cointegration and error-correction modeling. Results of the cointegration and error-correction models indicate bidirectional causality between GDP and energy consumption for both South Korea and Singapore. However, results of the standard Granger causality tests show no causal relationship between GDP and energy consumption for South Korea and unidirectional causal relationship from energy consumption to GDP for Singapore
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
Nonparametric tests for equality of psychometric functions.
García-Pérez, Miguel A; Núñez-Antón, Vicente
2017-12-07
Many empirical studies measure psychometric functions (curves describing how observers' performance varies with stimulus magnitude) because these functions capture the effects of experimental conditions. To assess these effects, parametric curves are often fitted to the data and comparisons are carried out by testing for equality of mean parameter estimates across conditions. This approach is parametric and, thus, vulnerable to violations of the implied assumptions. Furthermore, testing for equality of means of parameters may be misleading: Psychometric functions may vary meaningfully across conditions on an observer-by-observer basis with no effect on the mean values of the estimated parameters. Alternative approaches to assess equality of psychometric functions per se are thus needed. This paper compares three nonparametric tests that are applicable in all situations of interest: The existing generalized Mantel-Haenszel test, a generalization of the Berry-Mielke test that was developed here, and a split variant of the generalized Mantel-Haenszel test also developed here. Their statistical properties (accuracy and power) are studied via simulation and the results show that all tests are indistinguishable as to accuracy but they differ non-uniformly as to power. Empirical use of the tests is illustrated via analyses of published data sets and practical recommendations are given. The computer code in MATLAB and R to conduct these tests is available as Electronic Supplemental Material.
Directory of Open Access Journals (Sweden)
C. Erdem HEPAKTAN
2009-01-01
Full Text Available Main reason of devaluation practises is to provide foreign trade balance by increasing official exchange rates.. As a result of devaluation when the import becomes expensive and the export cheap due to the fluctuating exchange rates, foreign trade balance is aimed with more exchange in and less exchange out for a country which has a trade deficit.But to fulfill this in the first place the total of foreign demand elasticity of export goods with domestic elasticity of import goods must be more than 1 which is known to be Marshall condition. The success of devaluation depends on altitude of supply elasticity of export goods.In terms of the success of devaluation another important topic is the absolute necessity of prevention of cost inflation which will probably become a reality in the increasing price of imported raw material,intermediate and capital goods after dealuation.In this study, pieced co-integration analyse tested between 1980-2008 period concerning whether Marshall-Lerner condition secured for Turkey. Marshall-Lerner condition doesn’t work properly for Turkey in the long term.
Directory of Open Access Journals (Sweden)
Andrew Phiri
2017-09-01
Full Text Available In this study, we examine linear and nonlinear cointegration and causal relations between unemployment and stock market returns in South Africa using quarterly data collected between 1994:Q1 and 2016:Q1. Our empirical results reveal significant cointegration effects between the time series in both linear and nonlinear models, even though both frameworks ultimately reject the notion of any causal relations between the variables. Collectively, our study rejects the notion of unemployment being a good predictor for stock market returns and neither do developments in the stock market have any effect on the unemployment rate. Such evidence advocates for weak-form efficiency in the JSE equity prices whereby unemployment data cannot help investors to predict the movement of future share prices and further suggests that policymakers cannot rely on stock market development as an avenue towards lowering the prevailingly high levels of unemployment as set in current macroeconomic policy objectives.
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....
A Cointegrated Regime-Switching Model Approach with Jumps Applied to Natural Gas Futures Prices
Directory of Open Access Journals (Sweden)
Daniel Leonhardt
2017-09-01
Full Text Available Energy commodities and their futures naturally show cointegrated price movements. However, there is empirical evidence that the prices of futures with different maturities might have, e.g., different jump behaviours in different market situations. Observing commodity futures over time, there is also evidence for different states of the underlying volatility of the futures. In this paper, we therefore allow for cointegration of the term structure within a multi-factor model, which includes seasonality, as well as joint and individual jumps in the price processes of futures with different maturities. The seasonality in this model is realized via a deterministic function, and the jumps are represented with thinned-out compound Poisson processes. The model also includes a regime-switching approach that is modelled through a Markov chain and extends the class of geometric models. We show how the model can be calibrated to empirical data and give some practical applications.
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...
Bootstrap Determination of the Co-integration Rank in Heteroskedastic VAR Models
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders; Taylor, A.M.Robert
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 VAR model which obtain under the reduced rank null hypothesis. They propose methods based on an i.i.d. bootstrap re-sampling 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 re-sampling scheme, when time-varying behaviour is present in either the conditional or unconditional variance of the innovations. We show that the bootstrap PLR tests are asymptotically correctly sized and...
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...
Bootstrap Determination of the Co-Integration Rank in Heteroskedastic VAR Models
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders; Taylor, A. M. Robert
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 VAR model which obtain under the reduced rank null hypothesis. They propose methods based on an i.i.d. bootstrap re-sampling 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 re-sampling scheme, when time-varying behaviour is present in either the conditional or unconditional variance of the innovations. We show that the bootstrap PLR tests are asymptotically correctly sized and...
Does PPP hold for Big Mac price or consumer price index? Evidence from panel cointegration
Chien-Fu Chen; Chung-Hua Shen; Chien-An Wang
2007-01-01
This paper examines the validity of purchasing power parity (PPP) using CPI and Big Mac prices. The benchmark model, i.e., the OLS method, which does not take nonstationarity into account, rejects the hypothesis of PPP regardless of prices used. We next use the panel cointegration method to consider the nonstationary nature of variables. Estimated results for CPI are mixed. The PPP is rejected when the nominal exchange rate is employed as the dependent variable but is not rejected when the pr...
Ben Jebli, Mehdi; Ben Youssef, Slim
2015-01-01
This paper uses the vector error correction model (VECM) and Granger causality tests to investigate short and long-run relationships between per capita carbon dioxide (CO2) emissions, real gross domestic product (GDP), renewable and non-renewable energy consumption, trade openness ratio and agricultural value added (AVA) in Tunisia spanning the period 1980-2011. The Johansen-Juselius test shows that all our considered variables are cointegrated. Short-run Granger causality tests reveal the ex...
An Empirical Model Of Fractionally Cointegrated Daily High And Low Stock Market Prices
Czech Academy of Sciences Publication Activity Database
Baruník, Jozef; Dvořáková, S.
2015-01-01
Roč. 45, č. 1 (2015), s. 193-206 ISSN 0264-9993 R&D Projects: GA ČR(CZ) GBP402/12/G097 Keywords : fractional cointegration * long memory * range * volatility * daily high and low prices Subject RIV: AH - Economics Impact factor: 0.997, year: 2015 http://library.utia.cas.cz/separaty/2014/E/barunik-0434888.pdf
Real exchange-rates, co-integration and purchasing power parity - Irish experience in the EMS
Thom, R
1989-01-01
Dickey-Fuller and Co-Integration techniques are used to test the hypothesis that co-movements in Irish nominal exchange rates and relative prices are consistent with the implications of Purchasing Power Parity. The data reject PPP between Ireland and the US. Results from Irish/UK and Irish/German data are less decisive against the possibility that linear combinations of the nominal exchange rate and corresponding relative prices are stationary series.
GOVERNMENT DEBT, INTEREST RATES AND INTERNATIONAL CAPITAL FLOWS: EVIDENCE FROM COINTEGRATION
Pene Kalulumia
2000-01-01
This paper examines the impact of government debt on interest rates in the United States, Germany, the United Kingdom and Canada. It builds on the general portfolio balance framework which allows for both direct and indirect tests of the link between public debt and interest rates, and uses the Johansen-Juselius multivariate cointegration techniques to perform these tests. Indirect tests in this model consist of investigating the debt impact on interest rates through the effects of debt on th...
Testing For Seasonal Cointegration and Error Correction: The U.S. Pecan Price-Inventory Relationship
Ibrahim, Mohammed; Florkowski, Wojciech J.
2005-01-01
Using time series data we examine behavior of pecan prices and inventories at zero and seasonal frequencies, given results of seasonal cointegration tests. Both, seasonally unadjusted and adjusted quarterly data are used (1991-2002). Results suggest that, first, shelled and total pecan inventories and shelled pecan prices have common unit roots at both the non-seasonal and seasonal frequencies; second, there is no long run equilibrium between pecan prices and shelled or total inventories when...
Cointegrating MiDaS Regressions and a MiDaS Test
J. Isaac Miller
2011-01-01
This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of the error term both serially and with the regressors, I find that nonlinear least squares consistent...
Micromachined thin-film sensors for SOI-CMOS co-integration
Laconte, Jean; Raskin, Jean-Pierre
2006-01-01
Co-integration of MEMS and MOS in SOI technology is promising and well demonstrated hereThe impact of Micromachining on SOI devices is deeply analyzed for the first timeInclude extensive TMAH etching, residual stress, microheaters, gas-flow sensors reviewResidual stresses in thin films need to be more and more monitored in MEMS designsTMAH micromachining is an attractive alternative to KOH.
Exchange Rate â€“ Relative Price Nonlinear Cointegration Relationship in Malaysia
Venus Khim-Sen Liew; Chee-Keong Choong; Evan Lau; Kian-Ping Lim
2005-01-01
The finding of exchange rateâ€“relative price nonlinear cointegration relationship in Malaysia, among others, suggests that nonlinear Purchasing Power Parity (PPP) equilibrium may be regarded as reference point in judging the short run misalignment of the Ringgit currency and thereby deducing effective policy actions. Moreover, economists who wish to extend the simple PPP exchange rate model into the more complicated monetary exchange models may do so comfortably, at least in the text of Mala...
The integration of major fuel source markets in China. Evidence from panel cointegration tests
International Nuclear Information System (INIS)
Ma, Hengyun; Oxley, Les
2010-01-01
The paper tests for energy price co-movement in China over the 'new regime' as part of a strategy to test for the existence of a national energy market. Panel cointegration test statistics suggest that not all energy commodities are spatially homogenous in prices and the processes of energy price cointegration are different over time and over groups of fuels. The statistics demonstrate China's gradual, spatially partial and idiosyncratic energy reform process. Coal and electricity price series have co-moved since 2003, while the national panel cointegration test statistics suggest that gasoline and diesel price series have co-moved since 1997. Regional panel tests also show that there are apparently differences in the emergence of energy price co-movement. This suggests that regional energy markets have emerged in China. One of the important lessons of the research is that an energy market has, to some extent, already emerged in China and, as a consequence, energy prices are much less distorted than previously. If correct, this fact is of major global significance both in terms of future environmental effects and future trade and investment negotiations as China is seen internationally as a 'market driven economy'. (author)
Multi-sample nonparametric treatments comparison in medical ...
African Journals Online (AJOL)
Multi-sample nonparametric treatments comparison in medical follow-up study with unequal observation processes through simulation and bladder tumour case study. P. L. Tan, N.A. Ibrahim, M.B. Adam, J. Arasan ...
Speaker Linking and Applications using Non-Parametric Hashing Methods
2016-09-08
nonparametric estimate of a multivariate density function,” The Annals of Math- ematical Statistics , vol. 36, no. 3, pp. 1049–1051, 1965. [9] E. A. Patrick...Speaker Linking and Applications using Non-Parametric Hashing Methods† Douglas Sturim and William M. Campbell MIT Lincoln Laboratory, Lexington, MA...with many approaches [1, 2]. For this paper, we focus on using i-vectors [2], but the methods apply to any embedding. For the task of speaker QBE and
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
Directory of Open Access Journals (Sweden)
Alejandro Jara
2011-04-01
Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E
2016-11-16
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
Transition redshift: new constraints from parametric and nonparametric methods
Energy Technology Data Exchange (ETDEWEB)
Rani, Nisha; Mahajan, Shobhit; Mukherjee, Amitabha [Department of Physics and Astrophysics, University of Delhi, New Delhi 110007 (India); Jain, Deepak [Deen Dayal Upadhyaya College, University of Delhi, New Delhi 110015 (India); Pires, Nilza, E-mail: nrani@physics.du.ac.in, E-mail: djain@ddu.du.ac.in, E-mail: shobhit.mahajan@gmail.com, E-mail: amimukh@gmail.com, E-mail: npires@dfte.ufrn.br [Departamento de Física Teórica e Experimental, UFRN, Campus Universitário, Natal, RN 59072-970 (Brazil)
2015-12-01
In this paper, we use the cosmokinematics approach to study the accelerated expansion of the Universe. This is a model independent approach and depends only on the assumption that the Universe is homogeneous and isotropic and is described by the FRW metric. We parametrize the deceleration parameter, q(z), to constrain the transition redshift (z{sub t}) at which the expansion of the Universe goes from a decelerating to an accelerating phase. We use three different parametrizations of q(z) namely, q{sub I}(z)=q{sub 1}+q{sub 2}z, q{sub II} (z) = q{sub 3} + q{sub 4} ln (1 + z) and q{sub III} (z)=½+q{sub 5}/(1+z){sup 2}. A joint analysis of the age of galaxies, strong lensing and supernovae Ia data indicates that the transition redshift is less than unity i.e. z{sub t} < 1. We also use a nonparametric approach (LOESS+SIMEX) to constrain z{sub t}. This too gives z{sub t} < 1 which is consistent with the value obtained by the parametric approach.
Nonparametric Integrated Agrometeorological Drought Monitoring: Model Development and Application
Zhang, Qiang; Li, Qin; Singh, Vijay P.; Shi, Peijun; Huang, Qingzhong; Sun, Peng
2018-01-01
Drought is a major natural hazard that has massive impacts on the society. How to monitor drought is critical for its mitigation and early warning. This study proposed a modified version of the multivariate standardized drought index (MSDI) based on precipitation, evapotranspiration, and soil moisture, i.e., modified multivariate standardized drought index (MMSDI). This study also used nonparametric joint probability distribution analysis. Comparisons were done between standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), MSDI, and MMSDI, and real-world observed drought regimes. Results indicated that MMSDI detected droughts that SPEI and/or SSMI failed to do. Also, MMSDI detected almost all droughts that were identified by SPEI and SSMI. Further, droughts detected by MMSDI were similar to real-world observed droughts in terms of drought intensity and drought-affected area. When compared to MMSDI, MSDI has the potential to overestimate drought intensity and drought-affected area across China, which should be attributed to exclusion of the evapotranspiration components from estimation of drought intensity. Therefore, MMSDI is proposed for drought monitoring that can detect agrometeorological droughts. Results of this study provide a framework for integrated drought monitoring in other regions of the world and can help to develop drought mitigation.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2018-01-30
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Li, Ke; Lin, Boqiang
2016-01-01
Enhancing energy technology innovation performance, which is widely measured by energy technology patents through energy technology research and development (R&D) activities, is a fundamental way to implement energy conservation and emission abatement. This study analyzes the effects of R&D investment activities, economic growth, and energy price on energy technology patents in 30 provinces of China over the period 1999–2013. Several unit root tests indicate that all the above variables are generated by panel unit root processes, and a panel cointegration model is confirmed among the variables. In order to ensure the consistency of the estimators, the Fully-Modified OLS (FMOLS) method is adopted, and the results indicate that R&D investment activities and economic growth have positive effects on energy technology patents while energy price has a negative effect. However, the panel error correction models indicate that the cointegration relationship helps to promote economic growth, but it reduces R&D investment and energy price in the short term. Therefore, market-oriented measures including financial support and technical transformation policies for the development of low-carbon energy technologies, an effective energy price mechanism, especially the targeted fossil-fuel subsidies and their die away mode are vital in promoting China's energy technology innovation. - Highlights: • Energy technology patents in China are analyzed. • Relationship between energy patents and funds for R&D activities are analyzed. • China's energy price system hinders energy technology innovation. • Some important implications for China's energy technology policy are discussed. • A panel cointegration model with FMOLS estimator is used.
International Nuclear Information System (INIS)
Shahbaz, Muhammad; Tang, Chor Foon; Shahbaz Shabbir, Muhammad
2011-01-01
The aim of this paper is to re-examine the relationship between electricity consumption, economic growth, and employment in Portugal using the cointegration and Granger causality frameworks. This study covers the sample period from 1971 to 2009. We examine the presence of a long-run equilibrium relationship using the bounds testing approach to cointegration within the Unrestricted Error-Correction Model (UECM). Moreover, we examine the direction of causality between electricity consumption, economic growth, and employment in Portugal using the Granger causality test within the Vector Error-Correction Model (VECM). As a summary of the empirical findings, we find that electricity consumption, economic growth, and employment in Portugal are cointegrated and there is bi-directional Granger causality between the three variables in the long-run. With the exception of the Granger causality between electricity consumption and economic growth, the rest of the variables are also bi-directional Granger causality in the short-run. Furthermore, we find that there is unidirectional Granger causality running from economic growth to electricity consumption, but no evidence of reversal causality. - Highlights: → We re-examine the relationship between electricity consumption, economic growth, and employment in Portugal. → The electricity consumption and economic growth is causing each other in the long-run. → In the short-run, economic growth Granger-cause electricity consumption, but no evidence of reversal causality. → Energy conservation policy will deteriorate the process of economic growth in the long-run. → Portugal should increase investment on R and D to design new energy savings technology.
DEFF Research Database (Denmark)
Carrao, Hugo; Sepulcre, Guadalupe; Horion, Stéphanie Marie Anne F
2013-01-01
This study evaluates the relationship between the frequency and duration of meteorological droughts and the subsequent temporal changes on the quantity of actively photosynthesizing biomass (greenness) estimated from satellite imagery on rainfed croplands in Latin America. An innovative non-parametric...... and non-supervised approach, based on the Fisher-Jenks optimal classification algorithm, is used to identify multi-scale meteorological droughts on the basis of empirical cumulative distributions of 1, 3, 6, and 12-monthly precipitation totals. As input data for the classifier, we use the gridded GPCC...... for the period between 1998 and 2010. The time-series analysis of vegetation greenness is performed during the growing season with a non-parametric method, namely the seasonal Relative Greenness (RG) of spatially accumulated fAPAR. The Global Land Cover map of 2000 and the GlobCover maps of 2005/2006 and 2009...
Directory of Open Access Journals (Sweden)
Ismail Aydogus
2013-01-01
Full Text Available Validity of globalization brings out the question of whether greater openness is a booster reason to have a bigger government. This issue has been started to be discussed in relevant literature since the late 1970s. In this context, the purpose of this study is to examine the linkage between trade openness and the size of the government in Turkey over the period 1974-2011. Using residual based co-integration approach, we fail to find an evidence of a long run relationship. In addition, we do not provide causal support of compensation hypothesis in Turkish economy.
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.
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...
Some ideas for improving quality of the index tracking based on cointegration
Directory of Open Access Journals (Sweden)
Damián Pastor
2016-11-01
Full Text Available Cointegration approach to the passive portfolio management enables to replicate the selected stock index and to construct a portfolio with profitability and risk similar to market. This paper analyzes several options for improving this method. It focuses on one of the key tasks, which is an estimate of long-run equilibrium relationship. Five different methods were proposed and compared. The results confirmed the relevance of using the Engle-Granger methodology in all previous surveys, but it also suggested some interesting properties related to the estimate of regression coefficients based on different variants of the Minkowski metric or to estimate regression equation without intercept.
On the identification of fractionally cointegrated VAR models with the F(d) condition
DEFF Research Database (Denmark)
Santucci de Magistris, Paolo; Carlini, Federico
for any choice of the lag-length when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......). This is a generalization of the well-known I(1) condition to the fractional case. Imposing a proper restriction on the fractional integration parameter, d, is sufficient to guarantee identification of all model parameters and the validity of the F(d) condition. The paper also illustrates the indeterminacy between...
Likelihood-based inference for cointegration with nonlinear error-correction
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders Christian
2010-01-01
We consider a class of nonlinear vector error correction models where the transfer function (or loadings) of the stationary relationships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long-run cointegration parameters, and the short-run parameters. Asymptotic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normality can be found. A simulation study...
Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach
International Nuclear Information System (INIS)
Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji
2010-01-01
This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)
Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach
Energy Technology Data Exchange (ETDEWEB)
Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji [Department of Economics, University of Ibadan, Ibadan (Nigeria)
2010-01-15
This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)
Risk and return: Long-run relations, fractional cointegration, and return predictability
DEFF Research Database (Denmark)
Bollerslev, Tim; Osterrieder, Daniela; Sizova, Natalia
2013-01-01
Univariate dependencies in market volatility, both objective and risk neutral, are best described by long-memory fractionally integrated processes. Meanwhile, the ex post difference, or the variance swap payoff reflecting the reward for bearing volatility risk, displays far less persistent dynamics...... are consistent with generalized long-run risk models and help explain why classical efforts of establishing a naïve return-volatility relation fail. We also estimate a fractionally cointegrated vector autoregression (CFVAR). The model-implied long-run equilibrium relation between the two variance variables...
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Directory of Open Access Journals (Sweden)
Saerom Park
Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
A nonparametric spatial scan statistic for continuous data.
Jung, Inkyung; Cho, Ho Jin
2015-10-20
Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.
Directory of Open Access Journals (Sweden)
Janesh Sami
2011-01-01
Full Text Available The current literature on the relationship between electricity, exports and economic growth is mixed. This paper examines the relationship between exports, electricity consumption and real income per capita in Japan using time series data from 1960-2007.We applied bounds testing procedure developed by Pesaran et al(2001 and found that there is cointegrating relationship between electricity consumption ,exports and economic growth. On establishing cointegration, the causal relationship electricity consumption, exports and economic investigation was investigated within a Vector Error Correction Model (VECM framework. We found that in the long run, there is causality from exports and real GDP per capita to electricity consumption.
Nonparametric regression using the concept of minimum energy
International Nuclear Information System (INIS)
Williams, Mike
2011-01-01
It has recently been shown that an unbinned distance-based statistic, the energy, can be used to construct an extremely powerful nonparametric multivariate two sample goodness-of-fit test. An extension to this method that makes it possible to perform nonparametric regression using multiple multivariate data sets is presented in this paper. The technique, which is based on the concept of minimizing the energy of the system, permits determination of parameters of interest without the need for parametric expressions of the parent distributions of the data sets. The application and performance of this new method is discussed in the context of some simple example analyses.
Directory of Open Access Journals (Sweden)
Nahid Hatam
2016-03-01
Full Text Available Increasing knowledge of people about health leads to raising the share of health expenditures in government budget continuously; although governors do not like this rise because of budget limitations. This study aimed to find the association between health expenditures and economic growth in ECO countries. We added health capital in Solow model and used the panel cointegration approach to show the importance of health expenditures in economic growth. For estimating the model, first we used Pesaran cross-sectional dependency test, after that we used Pesaran CADF unit root test, and then we used Westerlund panel cointegration test to show if there is a long-term association between variables or not. After that, we used chaw test, Breusch-Pagan test and Hausman test to find the form of the model. Finally, we used OLS estimator for panel data. Findings showed that there is a positive, strong association between health expenditures and economic growth in ECO countries. If governments increase investing in health, the total production of the country will be increased, so health expenditures are considered as an investing good. The effects of health expenditures in developing countries must be higher than those in developed countries. Such studies can help policy makers to make long-term decisions.
Hatam, Nahid; Tourani, Sogand; Homaie Rad, Enayatollah; Bastani, Peivand
2016-02-01
Increasing knowledge of people about health leads to raising the share of health expenditures in government budget continuously; although governors do not like this rise because of budget limitations. This study aimed to find the association between health expenditures and economic growth in ECO countries. We added health capital in Solow model and used the panel cointegration approach to show the importance of health expenditures in economic growth. For estimating the model, first we used Pesaran cross-sectional dependency test, after that we used Pesaran CADF unit root test, and then we used Westerlund panel cointegration test to show if there is a long-term association between variables or not. After that, we used chaw test, Breusch-Pagan test and Hausman test to find the form of the model. Finally, we used OLS estimator for panel data. Findings showed that there is a positive, strong association between health expenditures and economic growth in ECO countries. If governments increase investing in health, the total production of the country will be increased, so health expenditures are considered as an investing good. The effects of health expenditures in developing countries must be higher than those in developed countries. Such studies can help policy makers to make long-term decisions.
Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets
Directory of Open Access Journals (Sweden)
Erie Febrian
2014-11-01
Full Text Available Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar's (2006 framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1, GARCH(3,1, and GARCH (1,1, respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.
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...
The Economic Determinants of Bioenergy Trade Intensity in the EU-28: A Co-Integration Approach
Directory of Open Access Journals (Sweden)
Mohd Alsaleh
2018-02-01
Full Text Available This paper examines the dynamic effect of the economic determinants on bilateral trade intensity of the European Union (EU region’s bioenergy industry outputs. The authors adopt the panel co-integration model approach to estimate annual trade intensity data of the EU-28 countries’ bioenergy industry outputs from 1990 to 2013. This study investigated the long-term influence of the rate of real exchange, gross domestic product (GDP, and export price on the trade intensity of bioenergy industry applying fully modified oriented least square (FMOLS, dummy oriented least square (DOLS, and pooled mean group (PMG models. In the current study, the findings boost the empirical validity of the panel co-integration model through FMOLS, indicating that depreciation has improved the trade intensity. This study has further investigated, through the causality test, a distinct set of countries. FMOLS estimation does find proof of the long run improvement of trade intensity. Thus, the result shows that the gross domestic product (GDP and the real exchange rate have a positive and noteworthy influence on the EU-28 region trade intensity of the bioenergy industry. Moreover, the export price affects negatively and significantly the trade intensity of the bioenergy industry in the EU-28 countries.
Lange, C; Lyon, H; DeMeo, D; Raby, B; Silverman, EK; Weiss, ST
2003-01-01
We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the
Nonparametric Bayesian models for a spatial covariance.
Reich, Brian J; Fuentes, Montserrat
2012-01-01
A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
Efficient nonparametric estimation of causal mediation effects
Chan, K. C. G.; Imai, K.; Yam, S. C. P.; Zhang, Z.
2016-01-01
An essential goal of program evaluation and scientific research is the investigation of causal mechanisms. Over the past several decades, causal mediation analysis has been used in medical and social sciences to decompose the treatment effect into the natural direct and indirect effects. However, all of the existing mediation analysis methods rely on parametric modeling assumptions in one way or another, typically requiring researchers to specify multiple regression models involving the treat...
Adaptive nonparametric Bayesian inference using location-scale mixture priors
Jonge, de R.; Zanten, van J.H.
2010-01-01
We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if
The nonparametric bootstrap for the current status model
Groeneboom, P.; Hendrickx, K.
2017-01-01
It has been proved that direct bootstrapping of the nonparametric maximum likelihood estimator (MLE) of the distribution function in the current status model leads to inconsistent confidence intervals. We show that bootstrapping of functionals of the MLE can however be used to produce valid
Bayesian nonparametric system reliability using sets of priors
Walter, G.M.; Aslett, L.J.M.; Coolen, F.P.A.
2016-01-01
An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Nonparametric modeling of dynamic functional connectivity in fmri data
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus
2015-01-01
dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted...
Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.
Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen
2011-04-01
Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.
On the robust nonparametric regression estimation for a functional regressor
Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias
2009-01-01
On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...
A general approach to posterior contraction in nonparametric inverse problems
Knapik, Bartek; Salomond, Jean Bernard
In this paper, we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive contraction rates for the parameter of interest from contraction rates of the related
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
Czech Academy of Sciences Publication Activity Database
Černý, Alexandr; Koblas, M.
2004-01-01
Roč. 1, č. 1 (2004), s. 110-120 ISSN 1544-8037 Institutional research plan: CEZ:AV0Z7085904 Keywords : stock market integration * speed of information transmission * data frequency in cointegration and Granger causality tests Subject RIV: AH - Economics
Statistical decisions under nonparametric a priori information
International Nuclear Information System (INIS)
Chilingaryan, A.A.
1985-01-01
The basic module of applied program package for statistical analysis of the ANI experiment data is described. By means of this module tasks of choosing theoretical model most adequately fitting to experimental data, selection of events of definte type, identification of elementary particles are carried out. For mentioned problems solving, the Bayesian rules, one-leave out test and KNN (K Nearest Neighbour) adaptive density estimation are utilized
Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.
2011-05-01
Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation
Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab
2011-01-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work
Nonparametric variational optimization of reaction coordinates
Energy Technology Data Exchange (ETDEWEB)
Banushkina, Polina V.; Krivov, Sergei V., E-mail: s.krivov@leeds.ac.uk [Astbury Center for Structural Molecular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT (United Kingdom)
2015-11-14
State of the art realistic simulations of complex atomic processes commonly produce trajectories of large size, making the development of automated analysis tools very important. A popular approach aimed at extracting dynamical information consists of projecting these trajectories into optimally selected reaction coordinates or collective variables. For equilibrium dynamics between any two boundary states, the committor function also known as the folding probability in protein folding studies is often considered as the optimal coordinate. To determine it, one selects a functional form with many parameters and trains it on the trajectories using various criteria. A major problem with such an approach is that a poor initial choice of the functional form may lead to sub-optimal results. Here, we describe an approach which allows one to optimize the reaction coordinate without selecting its functional form and thus avoiding this source of error.
International Nuclear Information System (INIS)
Narayan, P.K.; Smyth, R.
2005-01-01
This paper reports estimates of the long- and short-run elasticities of residential demand for electricity in Australia using the bounds testing procedure to cointegration, within an autoregressive distributive lag framework. In the long run, we find that income and own price are the most important determinants of residential electricity demand, while temperature is significant some of the time and gas prices are insignificant. Our estimates of long-run income elasticity and price elasticity of demand are consistent with previous studies, although they are towards the lower end of existing estimates. As expected, the short-run elasticities are much smaller than the long-run elasticities, and the coefficients on the error-correction coefficients are small consistent with the fact that in the short-run energy appliances are fixed. (author)
International Nuclear Information System (INIS)
Fouquet, Roger
1995-01-01
Over a two-year period, which started in April 1994, the real price of energy to UK households was expected to rise by 17.5% as a result of value-added tax (VAT) introduction. The regressive nature of the tax forced the government to limit VAT on residential fuel to 8%. Using a cointegration approach, to take account of the non-stationarity fuel consumption time series, this paper estimates real energy and fuel specific price and income elasticities for the period 1974:1-1994:1. They suggest that natural gas has a positive real energy price elasticity indicating that, as real price of energy rises, households scrap inefficient heaters and invest in more efficient ones, principally natural gas. These estimates enable projections to be made of the impact of the introduction of VAT and imply a rise in natural gas consumption as a result of the additional VAT, although at the expense of other less efficient fuels. (author)
Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach
Directory of Open Access Journals (Sweden)
Jeyhun I. Mikayilov
2017-11-01
Full Text Available Recent literature has shown that electricity demand elasticities may not be constant over time and this has investigated using time-varying estimation methods. As accurate modeling of electricity demand is very important in Azerbaijan, which is a transitional country facing significant change in its economic outlook, we analyze whether the response of electricity demand to income and price is varying over time in this economy. We employed the Time-Varying Coefficient cointegration approach, a cutting-edge time-varying estimation method. We find evidence that income elasticity demonstrates sizeable variation for the period of investigation ranging from 0.48% to 0.56%. The study has some useful policy implications related to the income and price aspects of the electricity consumption in Azerbaijan.
International Nuclear Information System (INIS)
Ghosh, Sajal
2009-01-01
This study probes nexus between electricity supply, employment and real GDP for India within a multivariate framework using autoregressive distributed lag (ARDL) bounds testing approach of cointegration. Long-run equilibrium relationship has been established among these variables for the time span 1970-71 to 2005-06. The study further establishes long- and short-run Granger causality running from real GDP and electricity supply to employment without any feedback effect. Thus, growth in real GDP and electricity supply are responsible for the high level of employment in India. The absence of causality running from electricity supply to real GDP implies that electricity demand and supply side measures can be adopted to reduce the wastage of electricity, which would not affect future economic growth of India.
Directory of Open Access Journals (Sweden)
Man Fu
2011-12-01
Full Text Available We study fluctuations in stock prices using a framework derived from the present value model augmented with a macroeconomic factor. The fundamental value is derived as the expected present discounted value of broad dividends that include, in addition to traditional cash dividends, other payouts to shareholders. A stochastic discount factor motivated by the consumption-based asset pricing model is utilized. A single macroeconomic factor, namely the output gap determines the non-fundamental component of stock prices. A resulting trivariate Vector Autoregression (TVAR model of stock prices, broad dividends, and the output gap shows evidence of cointegration in the DJIA and S&P 500 index data. Nonetheless, a sup augmented Dickey-Fuller test reveals existence of periodically collapsing bubbles in S&P 500 data during the late 1990s.
Estimating oil product demand in Indonesia using a cointegrating error correction model
International Nuclear Information System (INIS)
Dahl, C.
2001-01-01
Indonesia's long oil production history and large population mean that Indonesian oil reserves, per capita, are the lowest in OPEC and that, eventually, Indonesia will become a net oil importer. Policy-makers want to forestall this day, since oil revenue comprised around a quarter of both the government budget and foreign exchange revenues for the fiscal years 1997/98. To help policy-makers determine how economic growth and oil-pricing policy affect the consumption of oil products, we estimate the demand for six oil products and total petroleum consumption, using an error correction-cointegration approach, and compare it with estimates on a lagged endogenous model using data for 1970-95. (author)
International Nuclear Information System (INIS)
Cologni, Alessandro; Manera, Matteo
2008-01-01
Sharp increases in the price of oil are generally seen as a major contributor to business cycle asymmetries. Moreover, the very recent highs registered in the world oil market are causing concern about possible slowdowns in the economic performance of the most developed countries. In this paper a structural cointegrated VAR model has been considered for the G-7 countries in order to study the direct effects of oil price shocks on output and prices, and the reaction of monetary variables to external shocks. Our results can be summarized as follows: i) a stationary money demand, as suggested by the classic theory of money, can be identified for most countries; ii) according to the estimated coefficients of the structural part of the model, for all countries except Japan and U.K. the null hypothesis of an influence of oil prices on the inflation rate cannot be rejected. Inflation rate shocks are transmitted to the real economy by increasing interest rates; iii) impulse response analysis suggests, for most countries, the existence of an instantaneous, temporary effect of oil price innovations on prices; iv) impulse response functions indicate different monetary policy reactions to inflationary and growth shocks; v) the simulation exercises directed to estimate the total impact of the 1990 oil price shock indicate that for some countries (U.S.) a significant part of the effects of the oil price shock is due to the monetary policy reaction function. For other countries (Canada, France and Italy), however, the total impact is offset, at least in part, by an easing of monetary conditions. (author)
Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
Directory of Open Access Journals (Sweden)
Biqing Cai
2015-04-01
Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.
Nonparametric instrumental regression with non-convex constraints
International Nuclear Information System (INIS)
Grasmair, M; Scherzer, O; Vanhems, A
2013-01-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition. (paper)
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.
Deshwar, Amit G; Vembu, Shankar; Morris, Quaid
2015-01-01
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Directory of Open Access Journals (Sweden)
Xin Tian
2017-06-01
Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.
Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation
DEFF Research Database (Denmark)
Katebi, S.D.; Blanke, M.; Katebi, M.R.
This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...
Nonparametric Bayesian models through probit stick-breaking processes.
Rodríguez, Abel; Dunson, David B
2011-03-01
We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.
Exact nonparametric inference for detection of nonlinear determinism
Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene
2005-01-01
We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...
Non-parametric estimation of the individual's utility map
Noguchi, Takao; Sanborn, Adam N.; Stewart, Neil
2013-01-01
Models of risky choice have attracted much attention in behavioural economics. Previous research has repeatedly demonstrated that individuals' choices are not well explained by expected utility theory, and a number of alternative models have been examined using carefully selected sets of choice alternatives. The model performance however, can depend on which choice alternatives are being tested. Here we develop a non-parametric method for estimating the utility map over the wide range of choi...
Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data
CORNELIS A. LOS
2004-01-01
The efficiency of speculative markets, as represented by Fama's 1970 fair game model, is tested on weekly price index data of six Asian stock markets - Hong Kong, Indonesia, Malaysia, Singapore, Taiwan and Thailand - using Sherry's (1992) non-parametric methods. These scientific testing methods were originally developed to analyze the information processing efficiency of nervous systems. In particular, the stationarity and independence of the price innovations are tested over ten years, from ...
Investigation of MLE in nonparametric estimation methods of reliability function
International Nuclear Information System (INIS)
Ahn, Kwang Won; Kim, Yoon Ik; Chung, Chang Hyun; Kim, Kil Yoo
2001-01-01
There have been lots of trials to estimate a reliability function. In the ESReDA 20 th seminar, a new method in nonparametric way was proposed. The major point of that paper is how to use censored data efficiently. Generally there are three kinds of approach to estimate a reliability function in nonparametric way, i.e., Reduced Sample Method, Actuarial Method and Product-Limit (PL) Method. The above three methods have some limits. So we suggest an advanced method that reflects censored information more efficiently. In many instances there will be a unique maximum likelihood estimator (MLE) of an unknown parameter, and often it may be obtained by the process of differentiation. It is well known that the three methods generally used to estimate a reliability function in nonparametric way have maximum likelihood estimators that are uniquely exist. So, MLE of the new method is derived in this study. The procedure to calculate a MLE is similar just like that of PL-estimator. The difference of the two is that in the new method, the mass (or weight) of each has an influence of the others but the mass in PL-estimator not
Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.
2016-01-01
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration
Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.
Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.
International Nuclear Information System (INIS)
Honarvar, Afshin
2009-01-01
There is a common belief that gasoline prices respond more quickly to crude oil price increases than decreases. Some economists and politicians believe that asymmetry in oil and gasoline price movements is the outcome of a non-competitive gasoline market requiring that governments take policy action to address 'unfair pricing'. There is no consensus as to the existence, or nature, of the asymmetric relationship between prices of gasoline and crude oil. Much of this literature specifies asymmetry in the speed of adjustment and short-run adjustment coefficients. In contrast, Granger and Yoon's [Granger, C.W. and Yoon, G. 'Hidden Cointegration', University of California, San Diego, Department of Economics Working Paper, (2002).] Crouching Error Correction Model (CECM) identifies asymmetry of the cointegrating vectors between components (cumulative positive and negative changes) of the series. Applying the CECM to retail gasoline and crude oil prices for the U.S., we find that there is only evidence of cointegration between positive components of crude oil prices and negative components of gasoline prices. In contrast to the literature which attributes asymmetric price movements to market power of refiners, these findings suggest that gasoline prices -in the long run- are more influenced by the technological changes on the demand side than crude oil price movements on the supply side. (author)
Siciliani, Luigi
2006-01-01
Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure. Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency. This comparison was made using a sample of 17 Italian hospitals in the years 1996-9. Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications. This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
International Nuclear Information System (INIS)
Barat, Eric; Dautremer, Thomas
2007-01-01
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM
Nonparametric Analysis of Right Censored Data with Multiple Comparisons
Shih, Hwei-Weng
1982-01-01
This report demonstrates the use of a computer program written in FORTRAN for the Burroughs B6800 computer at Utah State University to perform Breslow's (1970) generalization of the Kruskal-Wallis test for right censored data. A pairwise multiple comparison procedure using Bonferroni's inequality is also introduced and demonstrated. Comparisons are also made with a parametric F test and the original Kruskal-Wallis test. Application of these techniques to two data sets indicate that there is l...
Online Nonparametric Bayesian Activity Mining and Analysis From Surveillance Video.
Bastani, Vahid; Marcenaro, Lucio; Regazzoni, Carlo S
2016-05-01
A method for online incremental mining of activity patterns from the surveillance video stream is presented in this paper. The framework consists of a learning block in which Dirichlet process mixture model is employed for the incremental clustering of trajectories. Stochastic trajectory pattern models are formed using the Gaussian process regression of the corresponding flow functions. Moreover, a sequential Monte Carlo method based on Rao-Blackwellized particle filter is proposed for tracking and online classification as well as the detection of abnormality during the observation of an object. Experimental results on real surveillance video data are provided to show the performance of the proposed algorithm in different tasks of trajectory clustering, classification, and abnormality detection.
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price......We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...
A Bayesian nonparametric approach to causal inference on quantiles.
Xu, Dandan; Daniels, Michael J; Winterstein, Almut G
2018-02-25
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.
Nonparametric statistics a step-by-step approach
Corder, Gregory W
2014-01-01
"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca
Evaluation of Nonparametric Probabilistic Forecasts of Wind Power
DEFF Research Database (Denmark)
Pinson, Pierre; Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg, orlov 31.07.2008
Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most...... likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set...
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Directory of Open Access Journals (Sweden)
Isaac Mugume
2016-01-01
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
Long-memory and the sea level-temperature relationship: a fractional cointegration approach.
Ventosa-Santaulària, Daniel; Heres, David R; Martínez-Hernández, L Catalina
2014-01-01
Through thermal expansion of oceans and melting of land-based ice, global warming is very likely contributing to the sea level rise observed during the 20th century. The amount by which further increases in global average temperature could affect sea level is only known with large uncertainties due to the limited capacity of physics-based models to predict sea levels from global surface temperatures. Semi-empirical approaches have been implemented to estimate the statistical relationship between these two variables providing an alternative measure on which to base potentially disrupting impacts on coastal communities and ecosystems. However, only a few of these semi-empirical applications had addressed the spurious inference that is likely to be drawn when one nonstationary process is regressed on another. Furthermore, it has been shown that spurious effects are not eliminated by stationary processes when these possess strong long memory. Our results indicate that both global temperature and sea level indeed present the characteristics of long memory processes. Nevertheless, we find that these variables are fractionally cointegrated when sea-ice extent is incorporated as an instrumental variable for temperature which in our estimations has a statistically significant positive impact on global sea level.
International Nuclear Information System (INIS)
Adom, Philip Kofi; Bekoe, William; Akoena, Sesi Kutri Komla
2012-01-01
In spite of the varying supply boosting efforts made by various governments to deal with the existing demand–supply gap in the electricity sector, the incessant growth in aggregate domestic electricity demand has made these efforts futile. As an objective, this paper attempts to identify the factors responsible for the historical growth trends in aggregate domestic electricity demand quantifying their effects both in the short-run and long-run periods using the ARDL Bounds cointegration approach and the sample period 1975 to 2005. In the long-run, real per capita GDP, industry efficiency, structural changes in the economy, and degree of urbanisation are identified as the main driving force behind the historical growth trend in aggregate domestic electricity demand. However, in the short-run, real per capita GDP, industry efficiency, and degree of urbanisation are the main drivers of aggregate domestic electricity demand. Industry efficiency is the only factor that drives aggregate domestic electricity demand downwards. However, the negative efficiency effect is insufficient to have outweighed the positive income, output, and demographic effects, hence the continual growth in aggregate domestic electricity demand. As a policy option, we recommend that appropriate electricity efficiency standards be implemented at the industry level. - Highlights: ► Real per capita GDP is the primary determinant of electricity demand both in the short and long-run. ► Industrial efficiency, structural changes and urbanisation rate play secondary role. ► The positive income, output, and demographic effects outweigh the negative efficiency effects.
International Nuclear Information System (INIS)
Wolde-Rufael, Yemane
2010-01-01
This paper attempts to examine the dynamic relationship between economic growth, nuclear energy consumption, labor and capital for India for the period 1969-2006. Applying the bounds test approach to cointegration developed by we find that there was a short- and a long-run relationship between nuclear energy consumption and economic growth. Using four long-run estimators we also found that nuclear energy consumption has a positive and a statistically significant impact on India's economic growth. Further, applying the approach to Granger causality and the variance decomposition approach developed by , we found a positive and a significant uni-directional causality running from nuclear energy consumption to economic growth without feedback. This implies that economic growth in India is dependent on nuclear energy consumption where a decrease in nuclear energy consumption may lead to a decrease in real income. For a fast growing energy-dependent economy this may have far-reaching implications for economic growth. India's economic growth can be frustrated if energy conservation measures are undertaken without due regard to the negative impact they have on economic growth.
Examining carbon emissions economic growth nexus for India: A multivariate cointegration approach
International Nuclear Information System (INIS)
Ghosh, Sajal
2010-01-01
The study probes cointegration and causality between carbon emissions and economic growth for India using ARDL bounds testing approach complemented by Johansen-Juselius maximum likelihood procedure in a multivariate framework by incorporating energy supply, investment and employment for time span 1971-2006. The study fails to establish long-run equilibrium relationship and long term causality between carbon emissions and economic growth; however, there exists a bi-directional short-run causality between the two. Hence, in the short-run, any effort to reduce carbon emissions could lead to a fall in the national income. This study also establishes unidirectional short-run causality running from economic growth to energy supply and energy supply to carbon emissions. The absence of causality running from energy supply to economic growth implies that in India, energy conservation and energy efficiency measures can be implemented to minimize the wastage of energy across value chain. Such measures would narrow energy demand-supply gap. Absence of long-run causality between carbon emissions and economic growth implies that in the long-run, focus should be given on harnessing energy from clean sources to curb carbon emissions, which would not affect the country's economic growth.
Short- and long-run demand for energy in Mexico: a cointegration approach
International Nuclear Information System (INIS)
Galindo, L.M.
2005-01-01
The objective of this paper is to estimate the demands for the different types of energy consumption for the Mexican economy over the period 1965-2001. These demands are modeled as a function of output and the own real price. The Johansen (J. Econ. Dynamics Control 12 (1988) 231) procedure and the likelihood ratio tests indicate the existence of long-run and stable relationships between each type of energy demand and income with the exception of the industrial sector where the cointegrating vector also includes the relative prices. The weak exogeneity tests indicate that energy consumption and income do not reject the null hypothesis of weak exogeneity when relative prices are weak exogenous. The final econometric models show that relative prices in the short run are relevant in all cases, with the exception of the residential sector. These results indicate that in Mexico the demand for energy is fundamentally driven by income and that the effect of the relative prices is basically concentrated on the short run with the exception of the industrial sector, which also shows a long-term price impact. The strong dependence of energy consumption with respect to income and the price inelastic response indicates that it is necessary to introduce strong measures to decouple energy consumption from output in order to obtain sustainable economic growth in Mexico
Directory of Open Access Journals (Sweden)
Roosaleh Laksono T.Y.
2016-04-01
Full Text Available Abstract. This study aims to analyze the effect of interest rate, inflation, and national income on rupiah exchange rate against dollar both long-term balanced relationship and short-run balance of empirical data from 1980-2015 (36 years using secondary data. The research method used is multiple linear regression methods of OLS. This research method used to approach with cointegration and error correction model (ECM by previously passing some other stages of statistical testing. The results of the study with cointegration (Johansen Cointegration test indicate that all the independent variables (inflation, national income, and interest rate and the non-free variable (exchange rate have a long-term equilibrium relationship, as evidenced by the test results Where the trace statistic value of 102.1727 is much greater than the critical value (5% of 47.85613. In addition, the result of Maximum Eigenvalue Statistic is the result of 36.7908 greater than the critical value of 5%. 27,584434. While the results of the model error correction test (ECM that only variable inflation, interest rates and residual significant, while the variable national income is not significant. This means that the inflation and interest rate variables have a short-run relationship to the exchange rate, it is seen from the Probability (Prob. Value of each variable is 0,05 (5%, besides the residual coefficient on the ECM test result is -0,732447, it shows that error correction term is 73,24% and significant. Keywords: Interest rate; Nasional income; Inflation; Exchange rate; Cointegration; Error Correction Model. Abstrak. Penelitian ini bertujuan untuk menganalisa pengaruh Suku bunga, inflasi, dan Pendapatan Nasional terhadap nilai tukar rupiah terhadap dollar baik hubungan keseimbangan jangka panjang maupun keseimbangan jangka pendek data empiris tahun 1980-2015 (36 tahun dengan menggunakan data sekunder. Metode penelitian yang digunakan adalah regresi
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
1st Conference of the International Society for Nonparametric Statistics
Lahiri, S; Politis, Dimitris
2014-01-01
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for NonParametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI, and other organizations. M.G. Akritas, S.N. Lahiri, and D.N. Politis are the first executive committee members of ISNPS, and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao, and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the wo...
Genomic breeding value estimation using nonparametric additive regression models
Directory of Open Access Journals (Sweden)
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Application of nonparametric statistics to material strength/reliability assessment
International Nuclear Information System (INIS)
Arai, Taketoshi
1992-01-01
An advanced material technology requires data base on a wide variety of material behavior which need to be established experimentally. It may often happen that experiments are practically limited in terms of reproducibility or a range of test parameters. Statistical methods can be applied to understanding uncertainties in such a quantitative manner as required from the reliability point of view. Statistical assessment involves determinations of a most probable value and the maximum and/or minimum value as one-sided or two-sided confidence limit. A scatter of test data can be approximated by a theoretical distribution only if the goodness of fit satisfies a test criterion. Alternatively, nonparametric statistics (NPS) or distribution-free statistics can be applied. Mathematical procedures by NPS are well established for dealing with most reliability problems. They handle only order statistics of a sample. Mathematical formulas and some applications to engineering assessments are described. They include confidence limits of median, population coverage of sample, required minimum number of a sample, and confidence limits of fracture probability. These applications demonstrate that a nonparametric statistical estimation is useful in logical decision making in the case a large uncertainty exists. (author)
Hastuti, S.; Harijono; Murtini, E. S.; Fibrianto, K.
2018-03-01
This current study is aimed to investigate the use of parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method. Ledre as Bojonegoro unique local food product was used as point of interest, in which 319 panelists were involved in the study. The result showed that ledre is characterized as easy-crushed texture, sticky in mouth, stingy sensation and easy to swallow. It has also strong banana flavour with brown in colour. Compared to eggroll and semprong, ledre has more variances in terms of taste as well the roll length. As RATA questionnaire is designed to collect categorical data, non-parametric approach is the common statistical procedure. However, similar results were also obtained as parametric approach, regardless the fact of non-normal distributed data. Thus, it suggests that parametric approach can be applicable for consumer study with large number of respondents, even though it may not satisfy the assumption of ANOVA (Analysis of Variances).
Bayesian nonparametric modeling for comparison of single-neuron firing intensities.
Kottas, Athanasios; Behseta, Sam
2010-03-01
We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.
Zhao, Yize; Kang, Jian; Yu, Tianwei
2014-06-01
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
The application of non-parametric statistical method for an ALARA implementation
International Nuclear Information System (INIS)
Cho, Young Ho; Herr, Young Hoi
2003-01-01
The cost-effective reduction of Occupational Radiation Dose (ORD) at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORD data of existing plants. Through the data analysis, it is required to identify what are the jobs of repetitive high ORD at the nuclear power plant. In this study, Percentile Rank Sum Method (PRSM) is proposed to identify repetitive high ORD jobs, which is based on non-parametric statistical theory. As a case study, the method is applied to ORD data of maintenance and repair jobs at Kori units 3 and 4 that are pressurized water reactors with 950 MWe capacity and have been operated since 1986 and 1987, respectively in Korea. The results was verified and validated, and PRSM has been demonstrated to be an efficient method of analyzing the data
International Nuclear Information System (INIS)
Bondia, Ripsy; Ghosh, Sajal; Kanjilal, Kakali
2016-01-01
Increasing greenhouse gas emissions, exhaustibility and geo-politics induced price volatility of crude oil has magnified the importance of looking for alternative sources of energy. In this paper, we investigate the long term relationship of stock prices of alternative energy companies with oil prices in a multivariate framework. To this end, we use threshold cointegration tests, which endogenously incorporate possible regime shifts in long run relationship of underlying variables. In contrast to the findings of the previous study by Managi and Okimoto (2013), our results indicate presence of cointegration among the variables with two endogenous structural breaks. This study confirms that ignoring the presence of structural breaks in a long time series data, as has been done in previous study, can produce misleading results. In terms of causality, while the stock prices of alternative energy companies are impacted by technology stock prices, oil prices and interest rates in the short run, there is no causality running towards prices of alternative energy stock prices in the long run. The study discusses the possible reasons behind the empirical findings and concludes with a discussion on short run and long run investment opportunities for the investors. - Highlights: • Cointegration between alternative energy companies stock price and oil price. • Threshold cointegration tests are employed. • Cointegration among the variables exists with two endogenous structural breaks. • Alternative energy companies stock price impacted by oil prices in short run. • No causality running towards prices of alternative energy stock prices in long run.
Nonparametric test of consistency between cosmological models and multiband CMB measurements
Energy Technology Data Exchange (ETDEWEB)
Aghamousa, Amir [Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 790-784 (Korea, Republic of); Shafieloo, Arman, E-mail: amir@apctp.org, E-mail: shafieloo@kasi.re.kr [Korea Astronomy and Space Science Institute, Daejeon 305-348 (Korea, Republic of)
2015-06-01
We present a novel approach to test the consistency of the cosmological models with multiband CMB data using a nonparametric approach. In our analysis we calibrate the REACT (Risk Estimation and Adaptation after Coordinate Transformation) confidence levels associated with distances in function space (confidence distances) based on the Monte Carlo simulations in order to test the consistency of an assumed cosmological model with observation. To show the applicability of our algorithm, we confront Planck 2013 temperature data with concordance model of cosmology considering two different Planck spectra combination. In order to have an accurate quantitative statistical measure to compare between the data and the theoretical expectations, we calibrate REACT confidence distances and perform a bias control using many realizations of the data. Our results in this work using Planck 2013 temperature data put the best fit ΛCDM model at 95% (∼ 2σ) confidence distance from the center of the nonparametric confidence set while repeating the analysis excluding the Planck 217 × 217 GHz spectrum data, the best fit ΛCDM model shifts to 70% (∼ 1σ) confidence distance. The most prominent features in the data deviating from the best fit ΛCDM model seems to be at low multipoles 18 < ℓ < 26 at greater than 2σ, ℓ ∼ 750 at ∼1 to 2σ and ℓ ∼ 1800 at greater than 2σ level. Excluding the 217×217 GHz spectrum the feature at ℓ ∼ 1800 becomes substantially less significance at ∼1 to 2σ confidence level. Results of our analysis based on the new approach we propose in this work are in agreement with other analysis done using alternative methods.
Aviral Kumar Tiwari
2011-01-01
This study has attempted to analyze the dynamics of renewable energy consumption, economic growth, and CO2 emissions. For the analysis, we used structural VAR approach. Results of unit root tests show that all variables are non-stationary at their level form and stationary in first difference form and cointegration analysis, analyzed through Johansen-Juselius (1990), shows that there is no evidence of cointegration among the test variables. The innovations analysis of study reveals that a pos...
Prior processes and their applications nonparametric Bayesian estimation
Phadia, Eswar G
2016-01-01
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and P...
Nonparametric autocovariance estimation from censored time series by Gaussian imputation.
Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K
2009-02-01
One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.
Debt and growth: A non-parametric approach
Brida, Juan Gabriel; Gómez, David Matesanz; Seijas, Maria Nela
2017-11-01
In this study, we explore the dynamic relationship between public debt and economic growth by using a non-parametric approach based on data symbolization and clustering methods. The study uses annual data of general government consolidated gross debt-to-GDP ratio and gross domestic product for sixteen countries between 1977 and 2015. Using symbolic sequences, we introduce a notion of distance between the dynamical paths of different countries. Then, a Minimal Spanning Tree and a Hierarchical Tree are constructed from time series to help detecting the existence of groups of countries sharing similar economic performance. The main finding of the study appears for the period 2008-2016 when several countries surpassed the 90% debt-to-GDP threshold. During this period, three groups (clubs) of countries are obtained: high, mid and low indebted countries, suggesting that the employed debt-to-GDP threshold drives economic dynamics for the selected countries.
Indoor Positioning Using Nonparametric Belief Propagation Based on Spanning Trees
Directory of Open Access Journals (Sweden)
Savic Vladimir
2010-01-01
Full Text Available Nonparametric belief propagation (NBP is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST created by breadth first search (BFS method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks. Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.
Pan, Wei
2003-07-22
Recently a class of nonparametric statistical methods, including the empirical Bayes (EB) method, the significance analysis of microarray (SAM) method and the mixture model method (MMM), have been proposed to detect differential gene expression for replicated microarray experiments conducted under two conditions. All the methods depend on constructing a test statistic Z and a so-called null statistic z. The null statistic z is used to provide some reference distribution for Z such that statistical inference can be accomplished. A common way of constructing z is to apply Z to randomly permuted data. Here we point our that the distribution of z may not approximate the null distribution of Z well, leading to possibly too conservative inference. This observation may apply to other permutation-based nonparametric methods. We propose a new method of constructing a null statistic that aims to estimate the null distribution of a test statistic directly. Using simulated data and real data, we assess and compare the performance of the existing method and our new method when applied in EB, SAM and MMM. Some interesting findings on operating characteristics of EB, SAM and MMM are also reported. Finally, by combining the idea of SAM and MMM, we outline a simple nonparametric method based on the direct use of a test statistic and a null statistic.
Exact nonparametric confidence bands for the survivor function.
Matthews, David
2013-10-12
A method to produce exact simultaneous confidence bands for the empirical cumulative distribution function that was first described by Owen, and subsequently corrected by Jager and Wellner, is the starting point for deriving exact nonparametric confidence bands for the survivor function of any positive random variable. We invert a nonparametric likelihood test of uniformity, constructed from the Kaplan-Meier estimator of the survivor function, to obtain simultaneous lower and upper bands for the function of interest with specified global confidence level. The method involves calculating a null distribution and associated critical value for each observed sample configuration. However, Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm provide the necessary tools for efficient computation of these exact bounds. Various aspects of the effect of right censoring on these exact bands are investigated, using as illustrations two observational studies of survival experience among non-Hodgkin's lymphoma patients and a much larger group of subjects with advanced lung cancer enrolled in trials within the North Central Cancer Treatment Group. Monte Carlo simulations confirm the merits of the proposed method of deriving simultaneous interval estimates of the survivor function across the entire range of the observed sample. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. It was begun while the author was visiting the Department of Statistics, University of Auckland, and completed during a subsequent sojourn at the Medical Research Council Biostatistics Unit in Cambridge. The support of both institutions, in addition to that of NSERC and the University of Waterloo, is greatly appreciated.
Hyperspectral image segmentation using a cooperative nonparametric approach
Taher, Akar; Chehdi, Kacem; Cariou, Claude
2013-10-01
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
Hunink, Maria; Bult, J.R.; De Vries, J; Weinstein, MC
1998-01-01
Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.
Zhang, Tingting; Kou, S C
2010-01-01
Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.
2000-09-01
The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Using nonparametrics to specify a model to measure the value of travel time
DEFF Research Database (Denmark)
Fosgerau, Mogens
2007-01-01
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....