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...
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....
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....
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...
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 ...
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...
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...
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....
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....
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
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....
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...
A General Representation Theorem for Integrated Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We study the algebraic structure of an I(d) vector autoregressive process, where d is restricted to be an integer. This is useful to characterize its polynomial cointegrating relations and its moving average representation, that is to prove a version of the Granger representation theorem valid...
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...
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert
Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, A. M. Robert
Many key macro-economic and …nancial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
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
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....
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....
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...
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...
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 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...
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....
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...
Directory of Open Access Journals (Sweden)
Jurgen A. Doornik
2017-11-01
Full Text Available This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1 and I(2 models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research.
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.
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
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
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.
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...
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.
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...
Estimation of pure autoregressive vector models for revenue series ...
African Journals Online (AJOL)
This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA; GENTON, MARC G.
2009-01-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided
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...
Oracle Inequalities for High Dimensional Vector Autoregressions
DEFF Research Database (Denmark)
Callot, Laurent; Kock, Anders Bredahl
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order...
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...
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
The Integration Order of Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2...
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...
vector bilinear autoregressive time series model and its superiority
African Journals Online (AJOL)
KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.
Vector bilinear autoregressive time series model and its superiority ...
African Journals Online (AJOL)
In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.
MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH
Directory of Open Access Journals (Sweden)
D. Tutberidze
2017-04-01
Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.
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...
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…
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....
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...
Circular Conditional Autoregressive Modeling of Vector Fields.
Modlin, Danny; Fuentes, Montse; Reich, Brian
2012-02-01
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.
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.
A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...
African Journals Online (AJOL)
This paper is an attempt to determine the presence and empirical significance of monetary policy and the bank lending view of the credit channel for Mauritius, which is particularly relevant at these times. A vector autoregressive (VAR) model of order three is used to examine the monetary transmission mechanism using ...
Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets
DEFF Research Database (Denmark)
Dias, Gustavo Fruet; Kapetanios, George
We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (...
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...
Exchange rate pass-through in Switzerland: Evidence from vector autoregressions
Jonas Stulz
2007-01-01
This study investigates the pass-through of exchange rate and import price shocks to different aggregated prices in Switzerland. The baseline analysis is carried out with recursively identified vector autoregressive (VAR) models. The data set comprises monthly observations, and pass-through effects are quantified by means of impulse response functions. Evidence shows that the exchange rate pass-through to import prices is substantial (although incomplete), but only moderate to total consumer ...
Bias-correction in vector autoregressive models
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
2014-01-01
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...
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.
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...
A representation theory for a class of vector autoregressive models for fractional processes
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
Based on an idea of Granger (1986), we analyze a new vector autoregressive model defined from the fractional lag operator 1-(1-L)^{d}. We first derive conditions in terms of the coefficients for the model to generate processes which are fractional of order zero. We then show that if there is a un...... root, the model generates a fractional process X(t) of order d, d>0, for which there are vectors ß so that ß'X(t) is fractional of order d-b, 0...
Use of (Time-Domain) Vector Autoregressions to Test Uncovered Interest Parity
Takatoshi Ito
1984-01-01
In this paper, a vector autoregression model (VAR) is proposed in order to test uncovered interest parity (UIP) in the foreign exchange market. Consider a VAR system of the spot exchange rate (yen/dollar), the domestic (US) interest rate and the foreign (Japanese) interest rate, describing the interdependence of the domestic and international financia lmarkets. Uncovered interest parity is stated as a null hypothesis that the current difference between the two interest rates is equal to the d...
Inflation adjustment in the open economy
DEFF Research Database (Denmark)
Nielsen, Heino Bohn; Bowdler, Christopher
2006-01-01
This paper estimates a cointegrated vector autoregressive (VAR) model for UK data on consumer prices, unit labour costs, import prices and real consumption growth. The estimated VAR indicates that the nominal variables are characterised by I(2) trends, and that a linear combination of these proce......This paper estimates a cointegrated vector autoregressive (VAR) model for UK data on consumer prices, unit labour costs, import prices and real consumption growth. The estimated VAR indicates that the nominal variables are characterised by I(2) trends, and that a linear combination...
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)
Bias-correction in vector autoregressive models: A simulation study
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....
Model reduction methods for vector autoregressive processes
Brüggemann, Ralf
2004-01-01
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities i...
Very-short-term wind power probabilistic forecasts by sparse vector autoregression
DEFF Research Database (Denmark)
Dowell, Jethro; Pinson, Pierre
2016-01-01
A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...
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.
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Directory of Open Access Journals (Sweden)
Akhsyim Afandi
2017-03-01
Full Text Available There was a question whether monetary policy works through bank lending channelrequired a monetary-induced change in bank loans originates from the supply side. Mostempirical studies that employed vector autoregressive (VAR models failed to fulfill thisrequirement. Aiming to offer a solution to this identification problem, this paper developed afive-variable vector error correction (VEC model of two separate bank credit markets inIndonesia. Departing from previous studies, the model of each market took account of onestructural break endogenously determined by implementing a unit root test. A cointegrationtest that took account of one structural break suggested two cointegrating vectors identifiedas bank lending supply and demand relations. The estimated VEC system for both marketssuggested that bank loans adjusted more strongly in the direction of the supply equation.
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Callot, Laurent
We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...
Business cycles and fertility dynamics in the United States: a vector autoregressive model.
Mocan, N H
1990-01-01
"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt
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....
Is There Any Sectoral Cointegration in Indonesia Equity Market?
Directory of Open Access Journals (Sweden)
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.
Bias-Correction in Vector Autoregressive Models: A Simulation Study
Directory of Open Access Journals (Sweden)
Tom Engsted
2014-03-01
Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J.; Engsted, T.
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J; Engsted, T
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
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
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.
Energy Technology Data Exchange (ETDEWEB)
Bunuel, M.
2001-07-01
The effects of technology and depletion on U. S. copper scarcity- as measured by real price can be estimated explicitly using econometrics, rather than assuming the their combined effect is implicit in a quadratic trend. The two most relevant cost-decreasing technologies-black-caving and open-pit mining and leaching and solvent extraction-electrowinning (SX-EW)-are proxied by their rate of diffusion. Depletion is proxied by average yield. Since these proxies and real price contain a unit root. Johansen's maximum-likelihood estimation procedure is used to test for the number of cointegrating relations, and estimate a vector autoregressive model from which a price equation in error-correction form is derived. A unique cointegrating relation without a trend is found, which supports the hypothesis that the real price of copper has no scarcity-rent component if we believe that this components should be modeled as a trend. The cointegrating relation loads into the price equation, and the convergence towards the long-run equilibrium is almost instantaneous. The estimated combined effect of physical depletion and technology on the U. S. real price of copper shows that the diffusion of block-caving and open-pit mining technologies off set the effect on price of the decline in average yield until the end of the thirties. From 1938 to 1976, the combined effect was slightly positive, and markedly and continuously negative thereafter, as a result of the introduction of leaching and SX-EW technologies, and the stabilization and even increase of average yield. (Author)
Debt Contagion in Europe: A Panel-Vector Autoregressive (VAR Analysis
Directory of Open Access Journals (Sweden)
Florence Bouvet
2013-12-01
Full Text Available The European sovereign-debt crisis began in Greece when the government announced in December, 2009, that its debt reached 121% of GDP (or 300 billion euros and its 2009 budget deficit was 12.7% of GDP, four times the level allowed by the Maastricht Treaty. The Greek crisis soon spread to other Economic and Monetary Union (EMU countries, notably Ireland, Portugal, Spain and Italy. Using quarterly data for the 2000–2011 period, we implement a panel-vector autoregressive (PVAR model for 11 EMU countries to examine the extent to which a rise in a country’s bond-yield spread or debt-to-GDP ratio affects another EMU countries’ fiscal and macroeconomic outcomes. To distinguish between interdependence and contagion among EMU countries, we compare results obtained for the pre-crisis period (2000–2007 with the crisis period (2008–2011 and control for global risk aversion.
METODE VECTOR AUTOREGRESSIVE (VAR DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI
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TJOK GDE SAHITYAHUTTI RANANGGA
2018-05-01
Full Text Available The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan arrival to Bali using vector autoregressive (VAR method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6.We used multivariate least square method to estimate the VAR(6’s parameters.The mean absolute percentage error (MAPE in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tourists. The prediction of Australian, Chinese, and Japanese tourists arrival to Bali for July 2017 to December 2017 tended to experience up and downs that were not too high compared to the previous months.
Public Capital and Regional Economic Growth: a SVAR Approach for the Spanish Regions
Directory of Open Access Journals (Sweden)
Geoffrey J. D. Hewings
2011-01-01
Full Text Available Recently, a significant share of the empirical analysis on the impact of public capital on regional growth has used multivariate time-series frameworks based on vector autoregressive (VAR models. Nevertheless, not as much attention has been dedicated to the analysis of the long-run determinants of regional growth processes using multi-region panel data and applying panel integration and co-integration techniques. This paper estimates the dynamic domestic effects of public infrastructure using a structural vector autoregressive (S-VAR methodology for the Spanish regions. From a methodological point of view, the paper contains several features that can be viewed as a contribution to the existing empirical literature. First, the important issues of the stationarity of the data and the existence and estimation of cointegrating relationships in the long-run are addressed in the context of the analysis of panel data. Secondly, the long-run cointegrating production function is embedded within structural vector error correction (S-VEC shortrun models to produce consistent estimates of impulse responses, contrary to many researchers who have estimated unrestricted VAR models in levels or VAR models in first differences. The estimates reveal new results with respect to the previous empirical evidence.
Is trade liberalization, economic growth, energy consumption good for the environment?
Jabeen, Asma
2015-01-01
The study attempts to identify the dynamic relationship between trade, income growth, energy consumption and CO2 emissions for Pakistan. Johansen's Cointegration procedure has been employed to estimate the coefficients of the Cointegrated Vector Autoregressive model. The results reveal that trade has a favorable effect on environmental quality for Pakistan economy, while income growth, energy consumption tends to worsen the environmental quality thus supporting the existence of Environmental ...
Is there an East-West split in North-American natural gas markets?
Serletis, Apostolos
1997-01-01
This paper presents evidence concerning shared stochastic trends in North American natural gas (spot) markets, using monthly data for the period that natural gas has been traded on organized exchanges (from June, 1990 to January, 1996). In doing so, it uses the Engle and Granger (1987) approach for estimating bivariate cointegrating relationships as well as Johansen's (1988) maximum likelihood approach for estimating cointegrating relationships in multivariate vector autoregressive models. Th...
DEFF Research Database (Denmark)
Lassen Kaspersen, Line; Føyn, Tullik Helene Ystanes
This paper investigates price transmission for agricultural commodities between world markets and the Ugandan market in an attempt to determine the impact of world market prices on the Ugandan market. Based on the realization that price formation is not a static concept, a dynamic vector...... price relations, i.e. the price variations between geographically separated markets in Uganda and the world markets. Our analysis indicates that food markets in Uganda, based on our study of sorghum price transmission, are not integrated into world markets, and that oil prices are a very determining...... autoregressive (VAR) model is presented. The prices of Robusta coffee and sorghum are examined, as both of these crops are important for the domestic economy of Uganda – Robusta as a cash crop, mainly traded internationally, and sorghum for consumption at household level. The analysis focuses on the spatial...
Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.
2007-11-01
In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
Unit root vector autoregression with volatility induced stationarity
DEFF Research Database (Denmark)
Rahbek, Anders; Nielsen, Heino Bohn
We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...
Unit Root Vector Autoregression with volatility Induced Stationarity
DEFF Research Database (Denmark)
Rahbek, Anders; Nielsen, Heino Bohn
We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...
Dealing with Multiple Solutions in Structural Vector Autoregressive Models.
Beltz, Adriene M; Molenaar, Peter C M
2016-01-01
Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.
On a Numerical and Graphical Technique for Evaluating some Models Involving Rational Expectations
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
On a numerical and graphical technique for evaluating some models involving rational expectations
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
Bildirici, Melike; Ersin, Özgür Ömer
2018-01-01
The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.
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...
Liu, Siwei; Molenaar, Peter C M
2014-12-01
This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.
Vector autoregressive model approach for forecasting outflow cash in Central Java
hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita
2018-05-01
Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.
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.
Testing Hypotheses About Glacial Cycles Against the Observational Record
DEFF Research Database (Denmark)
Kaufmann, Robert; Juselius, Katarina
2013-01-01
We estimate an identified cointegrated vector autoregression (CVAR) model of the climate system to test hypotheses about the physical mechanisms that may drive glacial cycles during the late Pleistocene. Results indicate that a permanent doubling of CO2 generates a 11.1oC rise in Antarctic...
Allowing the Data to Speak Freely
DEFF Research Database (Denmark)
Hoover, Kevin D.; Juselius, Katarina; Johansen, Søren
An explication of the key ideas behind the Cointegrated Vector Autoregression Approach. The CVAR approach is related to Haavelmo's famous "Probability Approach in Econometrics" (1944). It insists on careful stochastic specification as a necessary groundwork for econometric inference and the testing...... of economic theories. In time-series data, the probability approach requires careful specification of the integration and cointegration properties of variables in systems of equations. The relationship between the CVAR approach and wider methodological issues and between it and related approaches (e...
International Nuclear Information System (INIS)
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Energy Technology Data Exchange (ETDEWEB)
Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)
2017-01-15
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
A vector autoregressive model for electricity prices subject to long memory and regime switching
International Nuclear Information System (INIS)
Haldrup, Niels; Nielsen, Frank S.; Nielsen, Morten Oerregaard
2010-01-01
A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models. (author)
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
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)
The impact of oil-price shocks on Hawaii's economy: A case study using vector autoregression
International Nuclear Information System (INIS)
Gopalakrishnan, C.; Tian, X.; Tran, D.
1991-01-01
The effects of oil-price shocks on the macroeconomic performance of a non-oil-producing, oil-importing state are studied in terms of Hawaii's experience (1974-1986) using Vector Autoregression (VAR). The VAR model contains three macrovariables-real oil price, interest rate, and real GNP, and three regional variable-total civilian labor force, Honolulu consumer price index, and real personal income. The results suggested that oil-price shock had a positive effect on interest rate as well as local price (i.e., higher interest and higher local price), but a negative influence on real GNP. The negative income effect, however, was offset by the positive employment effect. The price of oil was found to be exogenous to all other variables in the system. The macrovariables exerted a pronounced impact on Hawaii's economy, most notably on consumer price
Sensor network based solar forecasting using a local vector autoregressive ridge framework
Energy Technology Data Exchange (ETDEWEB)
Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2016-04-04
The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.
Estimation bias and bias correction in reduced rank autoregressions
DEFF Research Database (Denmark)
Nielsen, Heino Bohn
2017-01-01
This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...
Identification of Civil Engineering Structures using Vector ARMA Models
DEFF Research Database (Denmark)
Andersen, P.
The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2007-01-01
textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent
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.
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...
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)
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.
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%.
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...
CAUSAL RELATIONSHIPS BETWEEN GRAIN, MEAT PRICES AND EXCHANGE RATES
Directory of Open Access Journals (Sweden)
Naveen Musunuru
2017-10-01
Full Text Available Understanding agricultural commodity price relationships are important as they help producers improve their awareness regarding production costs and ultimately aid in income determination. The present paper empirically examines the dynamic interrelationships among grain, meat prices and the U.S. dollar exchange rate. Johansen cointegration tests reveal no cointegrating relationships among the study variables. Majority of the commodities studied in the paper exhibited unidirectional causality except for corn and lean hogs. The vector autoregression (VAR model results indicate that the grain and meat prices are influenced by their own past prices. The role of exchange rates is found to be limited in linking the agricultural commodities.
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.
Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis
2018-01-01
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.
Thresholds and Smooth Transitions in Vector Autoregressive Models
DEFF Research Database (Denmark)
Hubrich, Kirstin; Teräsvirta, Timo
This survey focuses on two families of nonlinear vector time series models, the family of Vector Threshold Regression models and that of Vector Smooth Transition Regression models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the...
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 ...
Energy conservation and sustainable economic growth: The case of Latin America and the Caribbean
International Nuclear Information System (INIS)
Chang, Ching-Chih; Soruco Carballo, Claudia Fabiola
2011-01-01
This study examines the causal relationships among energy consumption, economic growth and carbon dioxide emissions in twenty countries from Latin America and the Caribbean region. The methodology includes the use of Phillips and Perron (PP) tests, a cointegration model with vector error correction modeling (VECM) and vector autoregression (VAR) with Granger causality. The study concludes that of the twenty countries analyzed, only in four of them will it be possible to implement energy conservation polices without affecting their economic growth, four others are not able to consider an energy conservation policy with economic growth, and the other twelve should focus on their economic growth before adopting any conservation policies. Energy efficiency was found in this region, especially in the countries which have both cointegration and short-term equilibrium. - Highlights: → Only four countries could implement energy conservation polices without affecting economic growth. → Twelve nations should focus on their economic growth before designing any energy conservation policies. → Energy efficiency was found in the countries which have both cointegration and short-term equilibrium.
Causality and cointegration analysis between macroeconomic variables and the Bovespa.
Directory of Open Access Journals (Sweden)
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%.
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)
Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach
Hamid, Mohd Fahmi Abdul; Shabri, Ani
2017-05-01
Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.
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.
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...
Modeling vector nonlinear time series using POLYMARS
de Gooijer, J.G.; Ray, B.K.
2003-01-01
A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector
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
Directory of Open Access Journals (Sweden)
Barli Suryanta
2014-08-01
Full Text Available Association of South East Asian Nations (ASEAN Economic Community (AEC 2020 has already been declared on 7 October 2003 by ASEAN Concord II in Bali, Indonesia. In general, AEC was designed to prepare ASEAN countries for ASEAN economic integration within the next 10-15 year. ASEAN Free Trade Area (AFTA had actually been launched since 1992 though was not comprehensive enough and kept ASEAN only partially integrated. To overcome it, ASEAN proposed inancial integration through capital market integration based on AEC commitment in order to reach comprehensive ASEAN economic integration. Indonesia is one of the ASEAN members that is linked by AEC 2020. The purpose of this paper is to evaluate Indonesian capital market co-integration in entering the ASEAN capital market integration compared to those of ASEAN 4. To examine the notion of the Indonesian capital market integration within ASEAN region, cointegration model is utilised to igure out co-integration between Indonesian stock market indices and ASEAN 4, i.e., Singapore, Malaysia, Philippines and Thailand. In addition, Vector Auto-regression (VAR model is also utilised to examine Indonesian market returns co-movement and dynamic link with ASEAN 4. The conclusions of this research, i.e. co-integration between Indonesian capital market with Singaporean, Malaysian, Philippines, and Thailand does not exist; there is neither co-movement nor strong dynamic link between Indonesian capital market with those of Singaporean, Malaysian, Philippines, and Thailand. This paper also recommends Indonesia to postpone the integration of its capital market into the integrated ASEAN capital market. ";} // -->activate javascript
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....
Suharsono, Agus; Aziza, Auliya; Pramesti, Wara
2017-12-01
Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship
Glacial cycles:exogenous orbital changes vs. endogenous climate dynamics
Kaufmann, R. K.; Juselius, Katarina
2010-01-01
We use a statistical model, the cointegrated vector autoregressive model, to assess the degree to which variations in Earth's orbit and endogenous climate dynamics can be used to simulate glacial cycles during the late Quaternary (390 kyr-present). To do so, we estimate models of varying complexity and compare the accuracy of their in-sample simulations. Results indicate that strong statistical associations between endogenous climate variables are not enough for statistical models to reproduc...
Ben Jebli, Mehdi; Ben Youssef, Slim
2013-01-01
We use the autoregressive distributed lag (ARDL) bounds testing approach for cointegration with structural breaks and the vector error correction model (VECM) Granger causality approach in order to investigate relationships between per capita CO2 emissions, GDP, renewable and non-renewable energy consumption and international trade (exports or imports) for Tunisia during the period 1980-2009. We show the existence of a short-run unidirectional causality running from trade, GDP, CO2 emission a...
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.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
2009-01-01
In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies...... to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and past observations. As a particular example, we consider...... an exponential autoregressive Poisson model for time series. Under geometric ergodicity, the maximum likelihood estimators are shown to be asymptotically Gaussian in the linear model. In addition, we provide a consistent estimator of their asymptotic covariance matrix. Our approach to verifying geometric...
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
System Estimation of Panel Data Models under Long-Range Dependence
DEFF Research Database (Denmark)
Ergemen, Yunus Emre
A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects...... and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed...
An improved ternary vector system for Agrobacterium-mediated rapid maize transformation.
Anand, Ajith; Bass, Steven H; Wu, Emily; Wang, Ning; McBride, Kevin E; Annaluru, Narayana; Miller, Michael; Hua, Mo; Jones, Todd J
2018-05-01
A simple and versatile ternary vector system that utilizes improved accessory plasmids for rapid maize transformation is described. This system facilitates high-throughput vector construction and plant transformation. The super binary plasmid pSB1 is a mainstay of maize transformation. However, the large size of the base vector makes it challenging to clone, the process of co-integration is cumbersome and inefficient, and some Agrobacterium strains are known to give rise to spontaneous mutants resistant to tetracycline. These limitations present substantial barriers to high throughput vector construction. Here we describe a smaller, simpler and versatile ternary vector system for maize transformation that utilizes improved accessory plasmids requiring no co-integration step. In addition, the newly described accessory plasmids have restored virulence genes found to be defective in pSB1, as well as added virulence genes. Testing of different configurations of the accessory plasmids in combination with T-DNA binary vector as ternary vectors nearly doubles both the raw transformation frequency and the number of transformation events of usable quality in difficult-to-transform maize inbreds. The newly described ternary vectors enabled the development of a rapid maize transformation method for elite inbreds. This vector system facilitated screening different origins of replication on the accessory plasmid and T-DNA vector, and four combinations were identified that have high (86-103%) raw transformation frequency in an elite maize inbred.
Kumaraswamy autoregressive moving average models for double bounded environmental data
Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme
2017-12-01
In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.
Energy Consumption and Economic Growth in Vietnam: Threshold Cointegration and Causality Analysis
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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.
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.
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.
Analysis of bank failure: An application of CVAR methodology on liquidity.
Mubanga Mpundu
2017-01-01
In this paper, balance sheet liquidity data was analyzed comprising of 157 Class I and 234 Class II banks. Class I banks are categorized as those with tier 1 capital in excess of $4 billion and internationally active while Class II banks are the rest. A Cointegrated Vector Autoregressive (CVAR) approach was used on balance sheet liquidity data to ascertain the behavior of variables in relation to bank failure. The study also demonstrated the nature of each of the variables containing estimate...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbæk, Anders; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, making an interpretation as an integer valued GARCH process possible. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model...
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.
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
Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus
2016-07-01
Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.
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...
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...
DYNAMICS OF MUTUAL FUNDS IN RELATION TO STOCK MARKET: A VECTOR AUTOREGRESSIVE CAUSALITY ANALYSIS
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Md. Shahadath Hossain
2013-01-01
Full Text Available In Bangladesh, primary and secondary mutual fund markets behave in a completely different way, where initial public offering (IPO investors of mutual funds earn more than 250 percent rerun, whereas secondary market investors cannot even manage to cover the opportunity cost of their investment. There are few other abnormalities present in this market – unlike everywhere in the world, most of the mutual funds are closed-end (92 percent and closed-end mutual funds are barred to issue bonus or right shares. A total of 714 day’s observations, from January 2008 to December 2010, of four variables– DSE (Dhaka Stock Exchange general index return, DSE general index turnover, mutual funds’ return and mutual funds’ turnover– are utilized. Stationarity of the variables are tested with Augmented Dickey-Fuller (ADF unit root test and found that variables are in different order of integration. Long-term equilibrium relationships among the variables are tested with Johansen cointegration and it is found that DSE general index return and mutual funds’ return are cointegrated. Toda-Yamamoto (TY version of granger non-causality test is employed and bidirectional causality is found moving from DSE (Dhaka Stock Exchange general index turnover to DSE general index return, whereas unidirectional causality is found moving from mutual fund’s return to DSE general index return, mutual funds’ return to mutual funds turnover, and DSE general index turnover to mutual funds turnover. This finding helps to conclude that equity shares’ demand drives the mutual funds demand but even higher demand of mutual funds fails to raise its own price unless underlying value of the mutual funds changes.
Langley, Tessa E; McNeill, Ann; Lewis, Sarah; Szatkowski, Lisa; Quinn, Casey
2012-11-01
To evaluate the effect of tobacco control media campaigns and pharmaceutical company-funded advertising for nicotine replacement therapy (NRT) on smoking cessation activity. Multiple time series analysis using structural vector autoregression, January 2002-May 2010. England and Wales. Tobacco control campaign data from the Central Office of Information; commercial NRT campaign data; data on calls to the National Health Service (NHS) stop smoking helpline from the Department of Health; point-of-sale data on over-the-counter (OTC) sales of NRT; and prescribing data from The Health Improvement Network (THIN), a database of UK primary care records. Monthly calls to the NHS stop smoking helpline and monthly rates of OTC sales and prescribing of NRT. A 1% increase in tobacco control television ratings (TVRs), a standard measure of advertising exposure, was associated with a statistically significant 0.085% increase in calls in the same month (P = 0.007), and no statistically significant effect in subsequent months. Tobacco control TVRs were not associated with OTC NRT sales or prescribed NRT. NRT advertising TVRs had a significant effect on NRT sales which became non-significant in the seasonally adjusted model, and no significant effect on prescribing or calls. Tobacco control campaigns appear to be more effective at triggering quitting behaviour than pharmaceutical company NRT campaigns. Any effect of such campaigns on quitting behaviour seems to be restricted to the month of the campaign, suggesting that such campaigns need to be sustained over time. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.
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)
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...
Prices, Wages and Fertility in Pre-Industrial England
DEFF Research Database (Denmark)
Klemp, Marc
A two-sector Malthusian model is formulated in terms of a cointegrated vector autoregressive (CVAR) model on error correction form. The model allows for both agricultural product wages and relative prices to affect fertility. The model is estimated using new data for the pre-industrial period...... in England, and the analysis reveals a strong, positive effect of agricultural wages as well as a small and, surprisingly, positive effect of real agricultural prices on fertility. Furthermore, it is demonstrated that there is constant returns to scale with respect to labour in the manufacturing sector...
Prices, wages and fertility in pre-industrial England
DEFF Research Database (Denmark)
Klemp, Marc Patrick Brag
2012-01-01
and relative prices to affect fertility. The model is estimated using new data for the pre-industrial period in England, and the analysis reveals a strong, positive effect of agricultural wages as well as a nonnegative effect of real agricultural prices on fertility. Furthermore, it is demonstrated......To shed light on the economic-demographic mechanisms operating in the epoch of pre-industrial economic stagnation, a two-sector Malthusian model is formulated in terms of a cointegrated vector autoregressive model on error correction form. The model allows for both agricultural product wages...
On a Graphical Technique for Evaluating Some Rational Expectations Models
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders R.
2011-01-01
Campbell and Shiller (1987) proposed a graphical technique for the present value model, which consists of plotting estimates of the spread and theoretical spread as calculated from the cointegrated vector autoregressive model without imposing the restrictions implied by the present value model....... In addition to getting a visual impression of the fit of the model, the purpose is to see if the two spreads are nevertheless similar as measured by correlation, variance ratio, and noise ratio. We extend these techniques to a number of rational expectation models and give a general definition of spread...
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.
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
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.
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.
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
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...
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)
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.
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)
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....
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2015-01-01
Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption. China's transport sector is highly energy-consuming and pollution-intensive. Between 1980 and 2012, the carbon dioxide emissions in China's transport sector increased approximately 9.7 times, with an average annual growth rate of 7.4%. Identifying the driving forces of the increase in carbon dioxide emissions in the transport sector is vital to developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the sector. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Private vehicles have more impact on emission reduction than cargo turnover due to the surge in private car population and its low energy efficiency. Urbanization also has significant effect on carbon dioxide emissions because of large-scale population movements and the transformation of the industrial structure. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the transport sector. - Highlights: • The driving forces of CO 2 emissions in China's transport sector were investigated. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to large-scale migration. • The role of private cars in reducing emissions is more important than cargo turnover
Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2016-01-01
Highlights: • We explore the driving forces of the iron and steel industry’s CO 2 emissions in China. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to mass real estate construction. • The role of economic growth in reducing emissions is more important than industrialization. - Abstract: Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO 2 ) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Urbanization also has significant effect on CO 2 emissions because of mass urban infrastructure and real estate construction. Economic growth has more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the iron and steel industry.
Directory of Open Access Journals (Sweden)
Alev Dilek Aydin
2015-01-01
Full Text Available The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
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)
Khan, Habib Nawaz; Razali, Radzuan B.; Shafei, Afza Bt.
2016-11-01
The objectives of this paper is two-fold: First, to empirically investigate the effects of an enlarged number of healthy and well-educated people on economic growth in Malaysia within the Endogeneous Growth Model framework. Second, to examine the causal links between education, health and economic growth using annual time series data from 1981 to 2014 for Malaysia. Data series were checked for the time series properties by using ADF and KPSS tests. Long run co-integration relationship was investigated with the help of vector autoregressive (VAR) method. For short and long run dynamic relationship investigation vector error correction model (VECM) was applied. Causality analysis was performed through Engle-Granger technique. The study results showed long run co-integration relation and positively significant effects of education and health on economic growth in Malaysia. The reported results also confirmed a feedback hypothesis between the variables in the case of Malaysia. The study results have policy relevance of the importance of human capital (health and education) to the growth process of the Malaysia. Thus, it is suggested that policy makers focus on education and health sectors for sustainable economic growth in Malaysia.
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.
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.
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. ...
COINTEGRATION ANALYSIS OF PURCHASING POWER PARITY IN REPUBLIC OF CROATIA
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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.
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
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...
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
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A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
Optimal transformations for categorical autoregressive time series
Buuren, S. van
1996-01-01
This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze
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....
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 ...
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.
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...
The Nexus between Higher Education and Economic Growth: An Empirical Investigation for Pakistan
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Amatul R. Chaudhary
2009-06-01
Full Text Available The study investigates the role of higher education in economic growth for Pakistan between 1972 and 2005 using the application of Johansen Cointegration and Toda & Yamamoto (1995 Causality approach in Vector Autoregressive (VAR framework. It examines whether higher education affect long run economic growth in Pakistan. The empirical analysis reveals that there is a long run relationship between economic growth and higher education, which suggests that these variables are necessary for each other. The empirical results of causality test indicate that there exists a unidirectional causality running from economic growth to higher education and no other direction of causality found between these variables.
Behavioural Pattern of Causality Parameter of Autoregressive ...
African Journals Online (AJOL)
In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...
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 ...
Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors
Hecq, Alain; Issler, J.V.; Telg, Sean
2017-01-01
The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into
A PANEL COINTEGRATION ANALYSIS: AN APPLICATION TO INTERNATIONAL TOURISM DEMAND OF THAILAND
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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.
Generalizing smooth transition autoregressions
DEFF Research Database (Denmark)
Chini, Emilio Zanetti
We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with part......We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail......, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling...
Bank Credit and Aggregate Import Demand in Nigeria: A Cointegration Analysis
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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.
Incorporating measurement error in n = 1 psychological autoregressive modeling
Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988
Directory of Open Access Journals (Sweden)
Jungho Baek
2011-03-01
Full Text Available This study examines the dynamic interrelationships between trade, income growth, energy consumption and CO2 emissions for G-20 economies in a framework of cointegrated vector autoregression (CVAR. Johansen's maximum likelihood procedure is used to estimate the coefficients of the cointegrated VAR. The results show that trade and income growth have a favorable effect on environmental quality for the developed G-20 member countries, while they have an adverse effect on the environment for the developing member countries. We also find that energy con- sumption tends to worsen environmental quality for both the developed and developing countries. Finally, it is found that trade and income to emission and energy causality holds for the developed countries; changes in degree of trade openness and income growth lead to corresponding changes in the rates of growth in emission and energy consumption. Emission and energy to trade and income causality, on the other hand, is found to hold for the developing countries; any shocks in emission and energy consumption cause corresponding fluctuations in income growth and trade openness.
Oil price shocks and long run price and import demand behavior
International Nuclear Information System (INIS)
Kleibergen, F.; Van Dijk, H.K.; Urbain, J.P.
1997-01-01
The effect which the oil price time series has on the long run properties of Vector AutoRegressive (VAR) models for price levels and import demand is investigated. As the oil price variable is assumed to be weakly exogenous for the long run parameters, a cointegration testing procedure allowing for weakly exogenous variables is developed using a LU decomposition of the long run multiplier matrix. The likelihood based cointegration test statistics, Wald, Likelihood Ratio and Lagrange Multiplier, are constructed and their limiting distributions derived. Using these tests, we find that incorporating the oil price in a model for the domestic or import price level of seven industrialized countries decreases the long run memory of the inflation rate. Second, we find that the results for import demand can be classified with respect to the oil importing or exporting status of the specific country. The result for Japan is typical as its import price is not influenced by gnp in the long run, which is the case for all other countries. 31 refs
Relationships among energy price shocks, stock market, and the macroeconomy: evidence from China.
Cong, Rong-Gang; Shen, Shaochuan
2013-01-01
This paper investigates the interactive relationships among China energy price shocks, stock market, and the macroeconomy using multivariate vector autoregression. The results indicate that there is a long cointegration among them. A 1% rise in the energy price index can depress the stock market index by 0.54% and the industrial value-adding growth by 0.037%. Energy price shocks also cause inflation and have a 5-month lag effect on stock market, which may result in the stock market "underreacting." The energy price can explain stock market fluctuations better than the interest rate over a longer time period. Consequently, investors should pay greater attention to the long-term effect of energy on the stock market.
Glacial cycles: exogenous orbital changes vs. endogenous climate dynamics
Kaufmann, R. K.; Juselius, K.
2010-04-01
We use a statistical model, the cointegrated vector autoregressive model, to assess the degree to which variations in Earth's orbit and endogenous climate dynamics can be used to simulate glacial cycles during the late Quaternary (390 kyr-present). To do so, we estimate models of varying complexity and compare the accuracy of their in-sample simulations. Results indicate that strong statistical associations between endogenous climate variables are not enough for statistical models to reproduce glacial cycles. Rather, changes in solar insolation associated with changes in Earth's orbit are needed to simulate glacial cycles accurately. Also, results suggest that non-linear dynamics, threshold effects, and/or free oscillations may not play an overriding role in glacial cycles.
DEFF Research Database (Denmark)
Kaufmann, R. K.; Juselius, Katarina
We use a statistical model, the cointegrated vector autoregressive model, to assess the degree to which variations in Earth's orbit and endogenous climate dynamics can be used to simulate glacial cycles during the late Quaternary (390 kyr-present). To do so, we estimate models of varying complexity...... and compare the accuracy of their in-sample simulations. Results indicate that strong statistical associations between endogenous climate variables are not enough for statistical models to reproduce glacial cycles. Rather, changes in solar insolation associated with changes in Earth's orbit are needed...... to simulate glacial cycles accurately. Also, results suggest that non-linear 10 dynamics, threshold effects, and/or free oscillations may not play an overriding role in glacial cycles....
Characteristics of the transmission of autoregressive sub-patterns in financial time series
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-09-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
Incorporating measurement error in n=1 psychological autoregressive modeling
Schuurman, Noemi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive
Asymmetric cointegration between exchange rate and trade balance in Nigeria
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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.
Pairs Trading to the Commodities Futures Market Using Cointegration Method
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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.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-01-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-08-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
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
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.
Ben Jebli, Mehdi; Ben Youssef, Slim
2017-01-01
We examine the dynamic relationships between per capita carbon dioxide (CO2) emissions, real gross domestic product (GDP), non-hydroelectric renewable energy (NHRE) consumption, agricultural value added (AVA), and agricultural land (AGRL) use for the case of Argentina over the period 1980-2013 by employing the autoregressive distributed lag (ARDL) bounds approach to cointegration and Granger causality tests. The Wald test confirms the existence of a long-run cointegration between variables. T...
The asymptotic and exact Fisher information matrices of a vector ARMA process
Klein, A.; Melard, G.; Saidi, A.
2008-01-01
The exact Fisher information matrix of a Gaussian vector autoregressive-moving average (VARMA) process has been considered for a time series of length N in relation to the exact maximum likelihood estimation method. In this paper it is shown that the Gaussian exact Fisher information matrix
Electricity consumption, industrial production, and entrepreneurship in Singapore
International Nuclear Information System (INIS)
Sun, Sizhong; Anwar, Sajid
2015-01-01
Within the context of a tri-variate vector autoregressive framework that includes entrepreneurship, this paper examines the link between electricity consumption and industrial production in Singapore's manufacturing sector. Unlike the existing studies, this paper focuses on one sector of the economy and utilises a unique monthly dataset. Empirical analysis based on Johansen's cointegration approach shows that the three variables are cointegrated – i.e., a stable long-run relationship exists among electricity consumption, output and entrepreneurship in Singapore's manufacturing sector. Empirical analysis based on data from January 1983 to February 2014 reveals that electricity consumption adjusts very slowly to shocks to industrial production and entrepreneurship. Furthermore, entrepreneurship Granger causes electricity consumption, which causes industrial production. As electricity consumption causes industrial output, the growth hypothesis concerning energy consumption and economic growth holds in Singapore's manufacturing sector and policies that restrict electricity production, without electricity imports, are likely to lead to a decline in the manufacturing output. - Highlights: • Using a unique monthly dataset, we focus on Singapore's manufacturing sector. • Electricity consumption, output and entrepreneurship are cointegrated. • Electricity consumption adjusts very slowly to shocks to the other variables. • Entrepreneurship causes electricity consumption which causes industrial production. • We find that growth hypothesis governs the electricity consumption and real output
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
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W. Wang
2005-01-01
Full Text Available Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average models for seasonal streamflow series. However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity effect, a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market
International Nuclear Information System (INIS)
Moutinho, Victor; Vieira, Joel; Carrizo Moreira, Antonio
2011-01-01
The main purpose of this article is twofold to analyze: (a) the long-term relation among the commodities prices and between spot electricity market price and commodity prices, and (b) the short-term dynamics among commodity prices and between electricity prices and commodity prices. Data between 2002 and 2005 from the Spanish electricity market was used. Econometric methods were used in the analysis of the commodity spot price, namely the vector autoregression model, the vector error correction model and the granger causality test. The co-integration approach was used to analyze the long-term relationship between the common stochastic trends of four fossil fuel prices. One of the findings in the long-term relation is that the prices of fuel and the prices of Brent are intertwined, though the prices of Brent ten to 'move' to reestablish the price equilibrium. Another finding is that the price of electricity is explained by the evolution of the natural gas series. - Highlights: → We model energy commodity prices in the Spanish electricity market. → We examine the short and long-term relationships among commodities prices. → We examine short and long-term relationships using co-integration techniques. → We found that in the long run the prices of fuel and Brent are intertwined. → The evolution of price of electricity is explained by the evolution of price of gas.
Essays on forecasting stationary and nonstationary economic time series
Bachmeier, Lance Joseph
This dissertation consists of three essays. Chapter II considers the question of whether M2 growth can be used to forecast inflation at horizons of up to ten years. A vector error correction (VEC) model serves as our benchmark model. We find that M2 growth does have marginal predictive content for inflation at horizons of more than two years, but only when allowing for cointegration and when the cointegrating rank and vector are specified a priori. When estimating the cointegration vector or failing to impose cointegration, there is no longer evidence of causality running from M2 growth to inflation at any forecast horizon. Finally, we present evidence that M2 needs to be redefined, as forecasts of the VEC model using data on M2 observed after 1993 are worse than the forecasts of an autoregressive model of inflation. Chapter III reconsiders the evidence for a "rockets and feathers" effect in gasoline markets. We estimate an error correction model of gasoline prices using daily data for the period 1985--1998 and fail to find any evidence of asymmetry. We show that previous work suffered from two problems. First, nonstationarity in some of the regressors was ignored, leading to invalid inference. Second, the weekly data used in previous work leads to a temporal aggregation problem, and thus biased estimates of impulse response functions. Chapter IV tests for a forecasting relationship between the volume of litigation and macroeconomic variables. We analyze annual data for the period 1960--2000 on the number of cases filed, real GDP, real consumption expenditures, inflation, unemployment, and interest rates. Bivariate Granger causality tests show that several of the macroeconomic variables can be used to forecast the volume of litigation, but show no evidence that the volume of litigation can be used to forecast any of the macroeconomic variables. The analysis is then extended to bivariate and multivariate regression models, and we find similar evidence to that of the
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.
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...
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbæk, Anders
In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing...... of non-stationary non-linear time series models. Thus the paper provides a full asymptotic theory for estimators as well as standard and non-standard test statistics. The derived asymptotic results prove to be new compared to results found elsewhere in the literature due to the impact of the estimated...... symmetric non-linear error correction considered. A simulation study shows that the fi…nite sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders
In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing...... of non-stationary non-linear time series models. Thus the paper provides a full asymptotic theory for estimators as well as standard and non-standard test statistics. The derived asymptotic results prove to be new compared to results found elsewhere in the literature due to the impact of the estimated...... symmetric non-linear error correction are considered. A simulation study shows that the finite sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....
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.
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.
Directory of Open Access Journals (Sweden)
Musa GÜN
2016-03-01
Full Text Available Credit default swap is also referred to as a credit derivative contract where the counterparty of the swap makes payments up until the maturity date of a financial contract. In this study, whether Gezi Park events which happened in 2013 are on a significant impact on Turkey credit default swap spread or not tested with the VAR(Vector Auto-Regressive method. In the analysis, investigated the long-term relationship with Johansen co-integration test and causality with Granger test. In addition, variance decomposition and impulse response analysis are performed. According to the results found significant correlations between Gezi Park events and CDS and also Eurobonds interest, the BIST 100 index, a basket of currencies with CDS spreads have been identified.
Modeling Real Exchange Rate Persistence in Chile
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Leonardo Salazar
2017-07-01
Full Text Available The long and persistent swings in the real exchange rate have for a long time puzzled economists. Recent models built on imperfect knowledge economics seem to provide a theoretical explanation for this persistence. Empirical results, based on a cointegrated vector autoregressive (CVAR model, provide evidence of error-increasing behavior in prices and interest rates, which is consistent with the persistence observed in the data. The movements in the real exchange rate are compensated by movements in the interest rate spread, which restores the equilibrium in the product market when the real exchange rate moves away from its long-run benchmark value. Fluctuations in the copper price also explain the deviations of the real exchange rate from its long-run equilibrium value.
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 ...
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...
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.
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, 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...
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.
A complex autoregressive model and application to monthly temperature forecasts
Directory of Open Access Journals (Sweden)
X. Gu
2005-11-01
Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
Greece Financial Crises and Sukuk Markets: Experience From Gulf Countries
Directory of Open Access Journals (Sweden)
Aldrin Herwany
2016-12-01
Full Text Available Many studies have been carried out to investigate the impact of recent European financial crises on the performance of financial instruments in other regions. Nevertheless, there have been insufficient studies explaining such impact on Islamic financial instrument. In particular, whether Greece Financial crises have affected performance of Sukuk traded in Gulf Markets needs to be answered. This study is aimed at empirically investigating the causality of credit and liquidity risk on Sukuk Markets in Gulf economies in the period of Greece Financial Crises. We analyzed the Sukuk data by employing Granger casuality test, with all the associated vector autoregression model procedures. Our findings show that Bahrain sukuk market is cointegrated with those of Qatar and UAE in the full period observation. Meanwhile, during the crisis, Qatar Sukuk market is cointegrated with those of UAE Bahrain. We also find that Bahrain Sukuk triggers market shock in both Qatar and UAE Sukuk markets. Bahrain consistently causes changes in price and spread of UAE Sukuk, both in the context of the full period and the during-crisis period.DOI: 10.15408/aiq.v9i1.3733
Hung-Pin, Lin
2014-01-01
The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries-United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.
Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
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Isao Ishida
2015-01-01
Full Text Available We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500 and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.
International Nuclear Information System (INIS)
Morishima, N.
1996-01-01
The multivariate autoregressive (MAR) modeling of a vector noise process is discussed in terms of the estimation of dominant noise sources in BWRs. The discussion is based on a physical approach: a transfer function model on BWR core dynamics is utilized in developing a noise model; a set of input-output relations between three system variables and twelve different noise sources is obtained. By the least-square fitting of a theoretical PSD on neutron noise to an experimental one, four kinds of dominant noise sources are selected. It is shown that some of dominant noise sources consist of two or more different noise sources and have the spectral properties of being coloured and correlated with each other. By diagonalizing the PSD matrix for dominant noise sources, we may obtain an MAR expression for a vector noise process as a response to the diagonal elements(i.e. residual noises) being white and mutually-independent. (Author)
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.
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.
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.
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)
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
A re-examination of the exchange rate overshooting hypothesis ...
African Journals Online (AJOL)
Southern African Business Review ... This finding is inconsistent with the monetary model of exchange rate determination, which asserts that there is a long-run relationship between the exchange rate ... Key words: Exchange rates, monetary model, autoregressive distributed lag, cointegration, exchange rate overshooting ...
Pemodelan Markov Switching Autoregressive
Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
2014-01-01
Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, tran...
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
Disaggregate energy consumption and industrial production in South Africa
International Nuclear Information System (INIS)
Ziramba, Emmanuel
2009-01-01
This paper tries to assess the relationship between disaggregate energy consumption and industrial output in South Africa by undertaking a cointegration analysis using annual data from 1980 to 2005. We also investigate the causal relationships between the various disaggregate forms of energy consumption and industrial production. Our results imply that industrial production and employment are long-run forcing variables for electricity consumption. Applying the [Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66, 225-250] technique to Granger-causality, we find bi-directional causality between oil consumption and industrial production. For the other forms of energy consumption, there is evidence in support of the energy neutrality hypothesis. There is also evidence of causality between employment and electricity consumption as well as coal consumption causing employment.
Testing competing forms of the Milankovitch hypothesis
DEFF Research Database (Denmark)
Kaufmann, Robert K.; Juselius, Katarina
2016-01-01
We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical...... ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship...... that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: Internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land...
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
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
2015-01-01
the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge...
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.
Zardad, Asma; Mohsin, Asma; Zaman, Khalid
2013-12-01
The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model (VECM) to estimate the changes in the volatility of real exchange rate series, while an error correction model was used to determine the short-run dynamics of the system. The study is limited to a few variables i.e., productivity differential (i.e., real GDP per capita relative to main trading partner); terms of trade; trade openness and government expenditures in order to manage robust data. The result indicates that real effective exchange rate (REER) has been volatile around its equilibrium level; while, the speed of adjustment is relatively slow. VECM results confirm long run convergence of real exchange rate towards its equilibrium level. Results from ARCH and GARCH estimation shows that real shocks volatility persists, so that shocks die out rather slowly, and lasting misalignment seems to have occurred.
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
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
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...
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.
Directory of Open Access Journals (Sweden)
Weni Hawariyuni
2012-01-01
Full Text Available Objective- Nowadays, numerous studies focus on the determinant of the demand for takaful, either family or general takaful in Malaysia. In this respect, these studies employ economic and socio-demographic variables to examine the determinants of takaful. They found that income, interest rates, financial development, pensions, stocks, price of insurance, life expectancy, dependency ratio, education, and age have positive on determinants of takaful. However, previous tudies have found that inflation, savings, and unemployment rate have negative relationship with the determinant of takaful in Malaysia. This study attempts to examine the determinants of the demand for family takaful and general takaful for comparative study in Malaysia for the period of 1988 to 2010. It employs economic and socio-demographic variables to measure these determinants.Methods-Using time series data, this study applies the Autoregressive Distributive Lag (ARDL approach to cointegration to examine the determinants of the demand for family takaful and general takaful in the short and long run.Result-The findings indicate that the economic and socio-demographic variables such as income, and education have positive relationship with the demand for family takaful in the short run, but not in the long run.Conclusion-Meanwhile, it also indicates that income and education have no relationship with demand for general takaful in Malaysia in the short run and long run. It indicates that if there is an increase in income, people tend to buy more family takaful of STMB. At the same time, the educated people already aware of the takaful products, they may necessarily purchase family takaful than life insurance.
Mathematical model with autoregressive process for electrocardiogram signals
Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de
2018-04-01
The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.
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
on the performance of Autoregressive Moving Average Polynomial
African Journals Online (AJOL)
Timothy Ademakinwa
Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag ... Moving Average Polynomial Distributed Lag (ARMAPDL) model. ..... Global Journal of Mathematics and Statistics. Vol. 1. ... Business and Economic Research Center.
Identification of BWR feedwater control system using autoregressive integrated model
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Andoh, Yasumasa; Yamamoto, Fumiaki; Idesawa, Masato; Itoh, Kazuo.
1983-01-01
With the view of contributing toward more reliable interpretation of noise behavior under normal operating conditions, which is essential for correct detection and/or diagnosis of incipient anomalies in nuclear power plants by noise analysis technique, studies has been undertaken of the noise behavior in a BWR feedwater control system, with use made of a multivariate autoregressive modeling technique. Noise propagation mechanisms as well as open- and closed-loop responses in the system are identified from noise data by a method in which an autoregressive integrated model is introduced. The closed-loop responses obtained with this method are compared with transient data from an actual test, and confirmed to be reliable in estimating semi-quantitative features. Other analyses performed with this model also yield results that appear most reasonable in their physical characteristics. These results have demonstrated the effectiveness of the noise analyses technique based on the autoregressive integrated model for evaluating and diagnosing the performance of feedwater control systems. (author)
Texture classification using autoregressive filtering
Lawton, W. M.; Lee, M.
1984-01-01
A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.
Kepler AutoRegressive Planet Search (KARPS)
Caceres, Gabriel
2018-01-01
One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.
To center or not to center? Investigating inertia with a multilevel autoregressive model
Directory of Open Access Journals (Sweden)
Ellen L. Hamaker
2015-01-01
Full Text Available Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion, cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship. This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction, cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.
International Nuclear Information System (INIS)
Ahmad, Ashfaq; Zhao, Yuhuan; Shahbaz, Muhammad; Bano, Sadia; Zhang, Zhonghua; Wang, Song; Liu, Ya
2016-01-01
This study investigates the long and short run relationships among carbon emissions, energy consumption and economic growth in India at the aggregated and disaggregated levels during 1971–2014. The autoregressive distributed lag model is employed for the cointegration analyses and the vector error correction model is applied to determine the direction of causality between variables. Results show that a long run cointegration relationship exists and that the environmental Kuznets curve is validated at the aggregated and disaggregated levels. Furthermore, energy (total energy, gas, oil, electricity and coal) consumption has a positive relationship with carbon emissions and a feedback effect exists between economic growth and carbon emissions. Thus, energy-efficient technologies should be used in domestic production to mitigate carbon emissions at the aggregated and disaggregated levels. The present study provides policy makers with new directions in drafting comprehensive policies with lasting impacts on the economy, energy consumption and environment towards sustainable development. - Highlights: •Relationships among carbon emissions, energy consumption and economic growth are investigated. •The EKC exists at aggregated and disaggregated levels for India. •All energy resources have positive effects on carbon emissions. •Gas energy consumption is less polluting than other energy sources in India.
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
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
Disaggregate energy consumption and industrial production in South Africa
Energy Technology Data Exchange (ETDEWEB)
Ziramba, Emmanuel [Department of Economics, University of South Africa, P.O Box 392, UNISA 0003 (South Africa)
2009-06-15
This paper tries to assess the relationship between disaggregate energy consumption and industrial output in South Africa by undertaking a cointegration analysis using annual data from 1980 to 2005. We also investigate the causal relationships between the various disaggregate forms of energy consumption and industrial production. Our results imply that industrial production and employment are long-run forcing variables for electricity consumption. Applying the [Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66, 225-250] technique to Granger-causality, we find bi-directional causality between oil consumption and industrial production. For the other forms of energy consumption, there is evidence in support of the energy neutrality hypothesis. There is also evidence of causality between employment and electricity consumption as well as coal consumption causing employment. (author)
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.
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
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...
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.
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.
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...
Analysis of Budget Deficits and Macroeconomic Fundamentals: A VAR-VECM Approach
Directory of Open Access Journals (Sweden)
Manamba Epaphra
2017-10-01
Full Text Available Aim/purpose - This paper examines the relationship between budget deficits and selected macroeconomic variables in Tanzania for the period spanning from 1966 to 2015. Design/methodology/approach - The paper uses Vector autoregression (VAR - Vector Error Correction Model (VECM and variance decomposition techniques. The Johansen's test is applied to examine the long run relationship among the variables under study. Findings - The Johansen's test of cointegration indicates that the variables are cointegrated and thus have a long run relationship. The results based on the VAR-VECM estimation show that real GDP and exchange rate have a negative and significant relationship with budget deficit whereas inflation, money supply and lending interest rate have a positive one. Variance decomposition results show that variances in the budget deficits are mostly explained by the real GDP, followed by inflation and real exchange rate. Research implications/limitations - Results are very indicative, but highlight the importance of containing inflation and money supply to check their effects on budget deficits over the short run and long-run periods. Also, policy recommendation calls for fiscal authorities in Tanzania to adopt efficient and effective methods of tax collection and public sector spending. Originality/value/contribution - Tanzania has been experiencing budget deficit since the 1970s and that this budget deficit has been blamed for high indebtedness, inflation and poor investment and growth. The paper contributes to the empirical debate on the causal relationship between budget deficits and macroeconomic variables by employing VAR-VECM and variance decomposition approaches.
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
Linear and non-linear autoregressive models for short-term wind speed forecasting
International Nuclear Information System (INIS)
Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.
2016-01-01
Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
Autoregressive Moving Average Graph Filtering
Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert
2016-01-01
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...
Interval Forecast for Smooth Transition Autoregressive Model ...
African Journals Online (AJOL)
In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...
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
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.
Does Expanding Higher Education Reduce Income Inequality in Emerging Economy? Evidence from Pakistan
Qazi, Wasim; Raza, Syed Ali; Jawaid, Syed Tehseen; Karim, Mohd Zaini Abd
2018-01-01
This study investigates the impact of development in the higher education sector, on the Income Inequality in Pakistan, by using the annual time series data from 1973 to 2012. The autoregressive distributed lag bound testing co-integration approach confirms the existence of long-run relationship between higher education and income inequality.…
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...
Levine, Matthew E; Albers, David J; Hripcsak, George
2016-01-01
Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.
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)
New interval forecast for stationary autoregressive models ...
African Journals Online (AJOL)
In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...
Multistage Stochastic Programming via Autoregressive Sequences
Czech Academy of Sciences Publication Activity Database
Kaňková, Vlasta
2007-01-01
Roč. 15, č. 4 (2007), s. 99-110 ISSN 0572-3043 R&D Projects: GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : Economic proceses * Multistage stochastic programming * autoregressive sequences * individual probability constraints Subject RIV: BB - Applied Statistics, Operational Research
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)
Prediction of Machine Tool Condition Using Support Vector Machine
International Nuclear Information System (INIS)
Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng
2011-01-01
Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.
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.
Application of autoregressive moving average model in reactor noise analysis
International Nuclear Information System (INIS)
Tran Dinh Tri
1993-01-01
The application of an autoregressive (AR) model to estimating noise measurements has achieved many successes in reactor noise analysis in the last ten years. The physical processes that take place in the nuclear reactor, however, are described by an autoregressive moving average (ARMA) model rather than by an AR model. Consequently more correct results could be obtained by applying the ARMA model instead of the AR model to reactor noise analysis. In this paper the system of the generalised Yule-Walker equations is derived from the equation of an ARMA model, then a method for its solution is given. Numerical results show the applications of the method proposed. (author)
Risk, opportunities and reasons of the household debt changes: The case of an emerging economy
Directory of Open Access Journals (Sweden)
Sisimogang Tracy Seane
2016-11-01
Full Text Available In the past decades, household debt in both developed and developing countries have been increasing. With an increase in the standard of living, household debt is also bound to increase. This paper examines the cointergation and causal link among household disposable income, household savings, debt service ratio, lending interest rate, consumer price index and household debt in South Africa. An Autoregressive Distributed Lag and Granger causality techniques was used to analyse data collected from the South African Reserve Bank and Quantec from 1984 to 2014. The results of Autoregressive Distributed Lag test revealed cointegrating relationships between household debt and debt service ratio as well as household debt and lending interest rate. However, there is no long run cointegrating relationship between household disposable income, household savings and consumer price index with household debt. The Granger causality results revealed that household disposable income, household savings, debt service ratio, lending interest rate, consumer price index do Granger cause household debt in South Africa. Policy makers should thus target these variables in order to reduce household debt in South Africa
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2010-01-01
This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...
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.
Empirical Vector Autoregressive Modeling
M. Ooms (Marius)
1993-01-01
textabstractChapter 2 introduces the baseline version of the VAR model, with its basic statistical assumptions that we examine in the sequel. We first check whether the variables in the VAR can be transformed to meet these assumptions. We analyze the univariate characteristics of the series.
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....
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
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.
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...
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...
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
Seasonality in the Austrian Economy: Common Seasonals and Forecasting
Kunst, Robert M.
1992-01-01
Abstract: Seasonal cointegration generalizes the idea of cointegration to processes with unit roots at frequencies different from 0. Here, also the dual notion of common trends, "common seasonals", is adopted for the seasonal case. Using a five-variable macroeconomic core system of the Austrian economy, it is demonstrated how common seasonals and seasonal cointegrating vectors look in practice. Statistical tests provide clear evidence on seasonal cointegration in the system. However, it is sh...
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...
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 ...
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…
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.
On the Temporal Causal Relationship Between Macroeconomic Variables
Directory of Open Access Journals (Sweden)
Srinivasan Palamalai
2014-02-01
Full Text Available The present study examines the dynamic interactions among macroeconomic variables such as real output, prices, money supply, interest rate (IR, and exchange rate (EXR in India during the pre-economic crisis and economic crisis periods, using the autoregressive distributed lag (ARDL bounds test for cointegration, Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on Vector Error Correction Model, variance decomposition analysis and impulse response functions. The empirical results reveal a stronger long-run bilateral relationship between real output, price level, IR, and EXR during the pre-crisis sample period. Moreover, the empirical results confirm a unidirectional short-run causality running from price level to EXR, IR to price level, and real output to money supply during the pre-crisis period. Also, it is evident from the test results that there exist short-run bidirectional relationships running between real output and EXR, price level and IR, and IR and EXR in the pre-crisis era, respectively. Most importantly, long-run bidirectional causality is found between real output, EXR, and IR during the economic crisis period. And the study results indicate short-run bidirectional causality between money supply and EXR, IR and price level, and IR and output in India during the crisis era. Also, a short-run unidirectional causality runs from prices to real output in the crisis period.
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...
Directory of Open Access Journals (Sweden)
Oktay TAŞ
2010-06-01
Full Text Available The main purpose of this study is to investigate stock market cointegration from the market efficiency perspective. Therefore, eleven emerging stock market indices are tested by using weekly data for the period of January 1998-December 2008 and for the sub period of January 2002-December 2008. Comovement among the emerging market countries was analyzed through Johansen cointegration test. The existence of two cointegrating vectors has been found at 5% significance level. However, the firm evidence against the market efficiency could not be established because of the low explanatory power of the results generated from the vector error correction model.
On robust forecasting of autoregressive time series under censoring
Kharin, Y.; Badziahin, I.
2009-01-01
Problems of robust statistical forecasting are considered for autoregressive time series observed under distortions generated by interval censoring. Three types of robust forecasting statistics are developed; meansquare risk is evaluated for the developed forecasting statistics. Numerical results are given.
Directory of Open Access Journals (Sweden)
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
Directory of Open Access Journals (Sweden)
Fei Jin
2013-05-01
Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression
Directory of Open Access Journals (Sweden)
Lili Li
2016-01-01
Full Text Available We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7 and China. The quantile autoregression model (QAR enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.
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.
Does OPEC act as a cartel? Empirical investigation of coordination behavior
International Nuclear Information System (INIS)
Kisswani, Khalid M.
2016-01-01
In this paper I use quarterly and monthly data from 1994 to 2014 to test if OPEC acts as a cartel, and therefore, it affects oil prices through members' coordination. I use Engle and Granger two-step approach, Johansen cointegration test and Autoregressive Distributed Lag (ARDL) bounds testing approach of cointegration to examine the long-run relation between OPEC production and each member's production as an evidence of coordination. Besides, I apply Granger causality and Toda and Yamamoto tests to check the direction of causality between the OPEC production and oil prices (U.K. Brent and Dubai Fateh). The findings show no evidence of cointegration between the production of the members and that of OPEC, indicating no cartel behavior exists. Moreover, the results show that OPEC production does not cause oil prices; rather it is the other way around. - Highlights: • I test if OPEC acts as a cartel; it affects oil prices through members' coordination. • I use cointegration to examine long run relation between OPEC production and member's production. • I test causality between the OPEC production and oil prices. • The findings show no evidence of cointegration indicating no cartel behavior exists. • The results show OPEC production does not cause oil prices; rather it is the other way around.
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.
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.
Autoregressive Processes in Homogenization of GNSS Tropospheric Data
Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.
2016-12-01
Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.
Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei
2018-01-01
Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.
Methodology for the AutoRegressive Planet Search (ARPS) Project
Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration
2018-01-01
The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.
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
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.
Business cycles in oil economies
International Nuclear Information System (INIS)
Al-Mutairi, N.H.
1991-01-01
This study examines the impact of oil price shocks on output fluctuations of several oil-exporting economies. In most studies of business cycles, the role of oil price is ignored; the few studies that use oil price as one of the variables in the system focus on modeling oil-importing economies. The vector autoregression (VAR) technique is used to consider the cases of Norway, Nigeria, and Mexico. Both atheoretical and 'structural' VARs are estimated to determine the importance of oil price impulses on output variations. The study reports two types of results: variance decomposition and impulse response functions, with particular emphasis on the issues of stationarity and co-integration among the series. The empirical results suggest that shocks to oil price are important in explaining output variations. In most cases, shocks to oil price are shown to explain more than 20% of the forecast variance of output over a 40-quarter horizon
Transmission of Shock across International Stock Markets: An Econometric Analysis
Directory of Open Access Journals (Sweden)
Shalini TALWAR
2018-05-01
Full Text Available The risk of spillover of volatility among international stock markets has increased manifold and it needs to be diagnosed comprehensively. In this paper, the authors have used 11 stock indices to identify influential markets and detect the direction of transmission of shock across markets in different time zones using Granger causality test, Johansen cointegration test and vector autoregression. The findings of VAR show that the forecast error at the 10-day horizon explained by their own innovation is highest for the Australian and Chinese markets followed by Japan, India, Brazil and Russia.Markets of Germany, UK, USA and Canada are influenced by the Australian market. In fact, the Australian market is seen to be the most influential market among the markets under the study. The impact of Chinese and Canadian markets is found to be the least. These results can be useful for optimal option valuation, effective portfolio allocation and performance benchmarking
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.
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...
Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)
DEFF Research Database (Denmark)
Agosto, Arianna; Cavaliere, Guiseppe; Kristensen, Dennis
We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn used...
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...
A re-examination of the relationship between electricity consumption and economic growth in Malaysia
International Nuclear Information System (INIS)
Tang, Chor Foon
2008-01-01
The purpose of this study is to re-investigate the relationship between electricity consumption and economic growth in Malaysia from 1972:1 to 2003:4. This study adopted the newly developed ECM-based F-test [Kanioura, A., Turner, P., 2005. Critical values for an F-test for cointegration in the multivariate model. Applied Economics 37(3), 265-270] for cointegration to examine the presence of long run equilibrium relationship through the autoregressive distributed lag (ARDL) model. The empirical evidence suggests that electricity consumption and economic growth are not cointegrated in Malaysia. However, the standard Granger's test and MWALD test suggest that electricity consumption and economic growth in Malaysia Granger causes each other. This finding provides policymakers with a better understanding of electricity consumption and allows them to formulate electricity consumption policy to support the economic development and to enhance the productivity of capital, labour and other factors of production for future economic growth in Malaysia
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
Estimation of the order of an autoregressive time series: a Bayesian approach
International Nuclear Information System (INIS)
Robb, L.J.
1980-01-01
Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases
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.
Directory of Open Access Journals (Sweden)
Mohammad Ghahremanzadeh
2014-06-01
Full Text Available Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR, as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004. The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996 was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use periodic autoregressive model for the future studies.
Directory of Open Access Journals (Sweden)
Usman M. Umer
2018-06-01
Full Text Available Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR and linear autoregressive (AR models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index. Keywords: Nonlinear time-series, Out-of-sample forecasting, Smooth transition autoregressive, Travel and leisure
Monthly streamflow forecasting with auto-regressive integrated moving average
Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani
2017-09-01
Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna
2018-05-23
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna; Ventrucci, Massimo; Rue, Haavard
2018-01-01
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
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)
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.
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.
Forecasting stock market averages to enhance profitable trading strategies
Haefke, Christian; Helmenstein, Christian
1995-01-01
In this paper we design a simple trading strategy to exploit the hypothesized distinct informational content of the arithmetic and geometric mean. The rejection of cointegration between the two stock market indicators supports this conjecture. The profits generated by this cheaply replicable trading scheme cannot be expected to persist. Therefore we forecast the averages using autoregressive linear and neural network models to gain a competitive advantage relative to other investors. Refining...
Co-movement of Foreign Direct and Portfolio Investments in Central and Eastern Europe
Yaman O. Erzurumlu; Giray Gozgor
2014-01-01
This paper empirically examines short- and long-run relationships between foreign direct investments (FDI) and volatility of foreign portfolio investments (FPI) in 12 Central and Eastern European (CEE) countries. We use the Generalized Autoregressive Conditional Heteroskedasticity models to calculate volatility of the FPIs. We utilize the second generation panel unit root test, panel-Wald causality test procedure and panel cointegration analysis allowing for structural breaks, and cross-secti...
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...
International Nuclear Information System (INIS)
Chen, Kuilin; Yu, Jie
2014-01-01
Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations
Etkin piyasa hipotezi ve gelişmekte olan piyasaların birlikte hareketi
Taş, Oktay; Tokmakçıoğlu, Kaya
2010-01-01
The main purpose of this study is to investigate stock market cointegration from the market efficiency perspective. Therefore, eleven emerging stock market indices are tested by using weekly data for the period of January 1998-December 2008 and for the sub period of January 2002-December 2008. Comovement among the emerging market countries was analyzed through Johansen cointegration test. The existence of two cointegrating vectors has been found at 5% significance level. However, the firm evi...
International Nuclear Information System (INIS)
Yao, Ruigen; Pakzad, Shamim N
2014-01-01
In the past few decades many types of structural damage indices based on structural health monitoring signals have been proposed, requiring performance evaluation and comparison studies on these indices in a quantitative manner. One tool to help accomplish this objective is analytical sensitivity analysis, which has been successfully used to evaluate the influences of system operational parameters on observable characteristics in many fields of study. In this paper, the sensitivity expressions of two damage features, namely the Mahalanobis distance of autoregressive coefficients and the Cosh distance of autoregressive spectra, will be derived with respect to both structural damage and measurement noise level. The effectiveness of the proposed methods is illustrated in a numerical case study on a 10-DOF system, where their results are compared with those from direct simulation and theoretical calculation. (paper)
Correlation, regression, and cointegration of nonstationary economic time series
DEFF Research Database (Denmark)
Johansen, Søren
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......), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coeffients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coe¢ cients do not converge to the relevant population...
Long-run Determinants of Technological Progress in Nigeria ...
African Journals Online (AJOL)
This study utilized time series data sets on TFP constructed based on purchasing power parity covering from 1960-2010 to estimate long run determinants of technological progress in Nigeria using Vector Error Correction Mode(VECM)l. The co-integration result shows evidence of two cointegrating equations while the Fully ...
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
Fiscal Imbalances, Poverty and Inequality in Pakistan
Irfan Ullah - Naimatullah Baber
2014-01-01
This paper has analyzed the fiscal imbalances, poverty and inequality with relevance to Pakistan. We use time series data from 1981 to 2010 and employ Autoregressive Distributed Lag Model (ARDL) to cointegration for estimation. The empirical findings suggest that fiscal deficits increase the poverty level and provide biases for inequality. Since deficit is financed through money supply, government debt and indirect taxes which threaten the purchasing power of the poor and drag them towards po...
MODEL PERMINTAAN UANG DI INDONESIA DENGAN PENDEKATAN VECTOR ERROR CORRECTION MODEL
Directory of Open Access Journals (Sweden)
imam mukhlis
2016-09-01
Full Text Available This research aims to estimate the demand for money model in Indonesia for 2005.2-2015.12. The variables used in this research are ; demand for money, interest rate, inflation, and exchange rate (IDR/US$. The stationary test with ADF used to test unit root in the data. Cointegration test applied to estimate the long run relationship berween variables. This research employed the Vector Error Correction Model (VECM to estimate the money demand model in Indonesia. The results showed that all the data was stationer at the difference level (1%. There were long run relationship between interest rate, inflation and exchange rate to demand for money in Indonesia. The VECM model could not explaine interaction between explanatory variables to independent variables. In the short run, there were not relationship between interest rate, inflation and exchange rate to demand for money in Indonesia for 2005.2-2015.12
Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery
Directory of Open Access Journals (Sweden)
Fan Liang
2013-02-01
Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.
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.
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)
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
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.
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.
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
The role of energy in economic growth.
Stern, David I
2011-02-01
This paper reviews the mainstream, resource economics, and ecological economics models of growth. A possible synthesis of energy-based and mainstream models is presented. This shows that when energy is scarce it imposes a strong constraint on the growth of the economy; however, when energy is abundant, its effect on economic growth is much reduced. The industrial revolution released the constraints on economic growth by the development of new methods of using coal and the discovery of new fossil fuel resources. Time-series analysis shows that energy and GDP cointegrate, and energy use Granger causes GDP when capital and other production inputs are included in the vector autoregression model. However, various mechanisms can weaken the links between energy and growth. Energy used per unit of economic output has declined in developed and some developing countries, owing to both technological change and a shift from poorer quality fuels, such as coal, to the use of higher quality fuels, especially electricity. Substitution of other inputs for energy and sectoral shifts in economic activity play smaller roles. © 2011 New York Academy of Sciences.
Longitudinal relationship between economic development and occupational accidents in China.
Song, Li; He, Xueqiu; Li, Chengwu
2011-01-01
The relativity between economic development and occupational accidents is a debated topic. Compared with the development courses of both economic development and occupational accidents in China during 1953-2008, this paper used statistic methods such as Granger causality test, cointegration test and impulse response function based on the vector autoregression model to investigate the relativity between economic development and occupational accidents in China from 1953 to 2008. Owing to fluctuation and growth scale characteristics of economic development, two dimensions including economic cycle and economic scale were divided. Results showed that there was no relationship between occupational accidents and economic scale during 1953-1978. Fatality rate per 10(5) workers was a conductive variable to gross domestic product per capita during 1979-2008. And economic cycle was an indicator to occupational accidents during 1979-2008. Variation of economic speed had important influence on occupational accidents in short term. Thus it is necessary to adjust Chinese occupational safety policy according to tempo variation of economic growth. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.
Evidence of Opportunity and Necessity Driven Entrepreneurship in Nigeria
Directory of Open Access Journals (Sweden)
Abubakar S. Garba
2013-01-01
Full Text Available The objective of this paper is to examine the influence of poverty, unemployment and GDP on entrepreneurship. Time series data for 31 years was collected from various official sources for the analysis. Vector autoregressive (VAR framework was adopted to systematically capture the rich dynamic of multiple time series. Other tests conducted were unit root test, Johansen and Juselius (1990 co-integration test, Granger causality and dynamic model analysis beyond the sample. It was found that poverty and GDP influence entrepreneurship negatively, while unemployment influences entrepreneurship positively. The paper reveals the presence of both opportunity and necessity driven entrepreneurs in the country. There is a need for the government to revisit the existing policy on micro, small and medium enterprises (MSMEs to adequately address the problem of the poor and unemployed by availing them with the opportunity to engage in entrepreneurship. Future study should consider mitigating the effect of frequent entry and exit from entrepreneurship in their data to correctly predict the effect of entrepreneurship on the economy.
Evaluation of Price Linkages within the Supply Chain of Rice ...
African Journals Online (AJOL)
This paper evaluates price linkages within the supply chain of rice markets in Cross River State using weekly prices in three urban markets located in major rice producing areas of the State. The Johansen cointegration test indicated one cointegrating vector both at the 1% and 5% level of significance. The results of the ...
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
Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input
Addo, Peter Martey
2014-01-01
This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.
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.
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)
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)
Chain binomial models and binomial autoregressive processes.
Weiss, Christian H; Pollett, Philip K
2012-09-01
We establish a connection between a class of chain-binomial models of use in ecology and epidemiology and binomial autoregressive (AR) processes. New results are obtained for the latter, including expressions for the lag-conditional distribution and related quantities. We focus on two types of chain-binomial model, extinction-colonization and colonization-extinction models, and present two approaches to parameter estimation. The asymptotic distributions of the resulting estimators are studied, as well as their finite-sample performance, and we give an application to real data. A connection is made with standard AR models, which also has implications for parameter estimation. © 2011, The International Biometric Society.
Bibi, Chan; Audi, Marc; Ali, Amjad
2017-01-01
This study has investigated the impact of gender inequality and environmental degradation on human well-being in the case of Pakistan from 1980 to 2014. Augmented Dickey-Fuller unit root test is used for stationarity of the variables. Autoregressive Distributed Lag model (ARDL) is used for co-integration among the variables of the model. The results show that gender inequality has a negative and significant impact on human well-being in Pakistan, while gender equality encourages human well-be...
The Environmental Consequences of Growth: Empirical Evidence from the Republic of Kazakhstan
Directory of Open Access Journals (Sweden)
Amantay Akbota
2018-03-01
Full Text Available The main objective of this paper is to examine the effect growth has on CO2 emissions in Kazakhstan, controlling for energy consumption, in the autoregressive distributed lag (ARDL cointegration framework. We find that the environmental Kuznets curve (EKC hypothesis seems to hold for Kazakhstan; this effect at a low level of income increases CO2 but at a high level decreases it. We also find that energy consumption increases CO2 emissions.
Stock Market Development and Economic Growth: Evidences from Asia-4 Countries
Azam, Muhammad; Haseeb, Muhammad; Samsi, Aznita binti; Raji, Jimoh Olajide
2016-01-01
The main purpose of this study is to examine the role of stock markets in economic growth for four Asian countries namely Bangladesh, India, China and Singapore. Annual time series cross country data over the period 1991 to 2012 and Autoregressive Distributed Lag (ARDL) bound testing approaches an analytical technique are used. Our results suggest that there is long-term cointegration among economic growth, Foreign Direct Investment (FDI), stock market development and inflation. The long-ter...
Zhai, Shiyan; Song, Genxin; Qin, Yaochen; Ye, Xinyue; Lee, Jay
2017-01-01
This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.
Directory of Open Access Journals (Sweden)
Shiyan Zhai
Full Text Available This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.
An RF-to-DC energy harvester for co-integration in a low-power 2.4 GHz transceiver frontend
Masuch, J.; Delgado-Restituto, M.; Milosevic, D.; Baltus, P.G.M.
2012-01-01
A 2.4 GHz energy harvester for co-integration into a low-power transceiver (TRx) operating at the same frequency is presented. An RF switch decouples the harvester from the TRx and keeps the performance degradation of the TRx low, i.e. 0.2 dB reduced output power in Tx-mode and 0.4 dB reduced
International Nuclear Information System (INIS)
Frank, T D; Mongkolsakulvong, S
2015-01-01
In a previous study strongly nonlinear autoregressive (SNAR) models have been introduced as a generalization of the widely-used time-discrete autoregressive models that are known to apply both to Markov and non-Markovian systems. In contrast to conventional autoregressive models, SNAR models depend on process mean values. So far, only linear dependences have been studied. We consider the case in which process mean values can have a nonlinear impact on the processes under consideration. It is shown that such models describe Markov and non-Markovian many-body systems with mean field forces that exhibit a nonlinear impact on single subsystems. We exemplify that such nonlinear dependences can describe order-disorder phase transitions of time-discrete Markovian and non-Markovian many-body systems. The relevant order parameter equations are derived and issues of stability and stationarity are studied. (paper)
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-06-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-03-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
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...
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.
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.
Another look on the relationships between oil prices and energy prices
International Nuclear Information System (INIS)
Lahiani, Amine; Miloudi, Anthony; Benkraiem, Ramzi; Shahbaz, Muhammad
2017-01-01
This paper employs the Quantile Autoregressive Distributed Lags (QARDL) model developed recently by Cho et al. (2015) to investigate the pass-through of oil prices to a set of energy prices. This approach allows analyzing simultaneously short-term connections and long-run cointegrating relationships across a range of quantiles. It also provides insights on the short-run predictive power of oil prices in predicting energy prices while accounting for the cointegration between oil prices and each of the considered energy prices in low, medium and high quantiles. Two key findings emerge from this paper. First, all considered energy prices are shown to be cointegrated with oil price across quantiles meaning that a stationaryequilibriumrelationship exists between single energy price and oil price. Second, we find evidence that oil price is a significant predictor of individual petroleum products prices and natural gas in the short run. This paper has important policy implications for forecasters, energy policy-makers and portfolio managers. - Highlights: • The pass-through of oil prices to a set of energy prices is investigated for US economy. • All considered energy prices are shown to be cointegrated with oil price across quantiles. • Oil price is a significant predictor of individual petroleum products prices in the short run. • Oil price also predicts natural gas prices in the short run.
Li, Weide; Kong, Demeng; Wu, Jinran
2017-01-01
Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.
Sun, Min-Chul; Kim, Garam; Kim, Sang Wan; Kim, Hyun Woo; Kim, Hyungjin; Lee, Jong-Ho; Shin, Hyungcheol; Park, Byung-Gook
2012-07-01
In order to extend the conventional low power Si CMOS technology beyond the 20-nm node without SOI substrates, we propose a novel co-integration scheme to build horizontal- and vertical-channel MOSFETs together and verify the idea using TCAD simulations. From the fabrication viewpoint, it is highlighted that this scheme provides additional vertical devices with good scalability by adding a few steps to the conventional CMOS process flow for fin formation. In addition, the benefits of the co-integrated vertical devices are investigated using a TCAD device simulation. From this study, it is confirmed that the vertical device shows improved off-current control and a larger drive current when the body dimension is less than 20 nm, due to the electric field coupling effect at the double-gated channel. Finally, the benefits from the circuit design viewpoint, such as the larger midpoint gain and beta and lower power consumption, are confirmed by the mixed-mode circuit simulation study.
Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model
Liu, Q. B.; Wang, Q. J.; Lei, M. F.
2015-09-01
It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.
Modeling money demand components in Lebanon using autoregressive models
International Nuclear Information System (INIS)
Mourad, M.
2008-01-01
This paper analyses monetary aggregate in Lebanon and its different component methodology of AR model. Thirteen variables in monthly data have been studied for the period January 1990 through December 2005. Using the Augmented Dickey-Fuller (ADF) procedure, twelve variables are integrated at order 1, thus they need the filter (1-B)) to become stationary, however the variable X 1 3,t (claims on private sector) becomes stationary with the filter (1-B)(1-B 1 2) . The ex-post forecasts have been calculated for twelve horizons and for one horizon (one-step ahead forecast). The quality of forecasts has been measured using the MAPE criterion for which the forecasts are good because the MAPE values are lower. Finally, a pursuit of this research using the cointegration approach is proposed. (author)
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...
On the Oracle Property of the Adaptive LASSO in Stationary and Nonstationary Autoregressions
DEFF Research Database (Denmark)
Kock, Anders Bredahl
We show that the Adaptive LASSO is oracle efficient in stationary and non-stationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
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.
Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K
2014-06-01
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Nabelek, Daniel P.; Ho, K. C.
2013-06-01
The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.
Is Panama on a sustainable development path?
Directory of Open Access Journals (Sweden)
Manuel A. Zambrano Monserrate
2018-04-01
Full Text Available This article examines the long-term relationship between CO2 emissions, GDP and energy consumption in Panama during the period 1971-2011 through an autoregressive model of distributed delays to verify the cointegration of variables in the long term. The results confirm the presence of an environmental Kuznets curve and that CO2 emissions and energy consumption from primary sources do not affect economic growth and that environmental degradation increases with energy consumption. Finally, it makes some policy recommendations.
Directory of Open Access Journals (Sweden)
Bambang Hermanto
2013-01-01
Full Text Available This research studies the international co-movement among Asia Pacific emerging markets stock price indices during the late 2000s recession by using the monthly observations start from 1st October 2001 until 1st April 2011. The co-integration analysis and parsimonious Vector Error Correction Model employed in this research reveal a long-term relationship and inter-dependencies among seven Asia Pacific emerging market stock price indices. This research finds that the unique co-integration exists on the equations. Specifically, two indices from China and Taiwan having meteor shower potential while the rest indices from Thailand, Malaysia, and Indonesia are known to have heat waves effects or country specific factors on the equation. Finally, all the results are linked to the international diversification strategies. Keywords: Co-movement, co-integration, emerging market, heat waves, meteor shower, Asia Pacific, inter-dependencies, Vector Error Correction Model, international diversification
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Bias-corrected estimation in potentially mildly explosive autoregressive models
DEFF Research Database (Denmark)
Haufmann, Hendrik; Kruse, Robinson
This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...
Solarin, Sakiru Adebola; Al-Mulali, Usama; Sahu, Pritish Kumar
2017-10-01
The main objective of this study is to investigate the influence of the globalisation (Trans-Pacific Partnership (TPP) agreement in particular) on air pollution in Malaysia. To achieve this goal, the Autoregressive Distributed Lag (ARDL) model, Johansen cointegration test and fully modified ordinary least square (FMOLS) methods are utilised. CO 2 emission is used as an indicator of pollution while GDP per capita and urbanisation serve as its other determinants. In addition, this study uses Malaysia's total trade with 10 TPP members as an indicator of globalisation and analyse its effect on CO 2 emission in Malaysia. The outcome of this research shows that the variables are cointegrated. Additionally, GDP per capita, urbanisation and trade between Malaysia and its 10 TPP partners have a positive impact on CO 2 emissions in general. Based on the outcome of this research, important policy implications are provided for the investigated country.
Sustainable Energy Development in Saudi Arabia
Directory of Open Access Journals (Sweden)
Mounir Belloumi
2015-04-01
Full Text Available The main objective of this research is to study the role of energy consumption in economic growth in Saudi Arabia over the period of 1971–2012 using the autoregressive distributed lag (ARDL cointegration procedure, and based on neoclassical growth, endogenous growth, and ecological-economics viewpoints. Our empirical results show the existence of a cointegrating relationship between the different variables investigated. In addition, all the inputs (conventional and non-conventional Granger cause economic growth in both the short and long runs. Our findings confirm the energy-led growth hypothesis in the case of Saudi Arabia. Hence, energy conservation policies may deteriorate economic growth in Saudi Arabia if they are not followed by measures that improve energy efficiency, energy saving technologies and encourage the investment and use of renewable energy sources such as solar and wind energies that can participate in the attenuation of climate changes.
High speed diesel consumption and economic growth in India
Energy Technology Data Exchange (ETDEWEB)
Ghosh, Sajal [Management Development Institute (MDI), Gurgaon 122001 (India)
2010-04-15
This study probes the long-term equilibrium relationship among High Speed Diesel (HSD) consumption, real GDP and price of HSD in India using autoregressive distributed lag (ARDL) bounds testing approach of cointegration for the time span 1972-1973 to 2005-2006. Empirical results reveal that the series are cointegrated and long term income elasticity for HSD demand in India is 1.27 while that for short-run is 0.46. Both long-run and short-run price elasticities are found to be statistically insignificant. The study also establishes a short-run bi-directional causality between economic growth and HSD consumption and the existence of a long-run unidirectional causality running from economic growth to HSD consumption. Finally, a set of policy prescriptions have been suggested to reduce the consumption of HSD, which should have no adverse impact on economy in the long-run. (author)
Are international securitized property markets converging or diverging?
Hui, Eddie C. M.; Chen, Jia; Chan, Ka Kwan Kevin
2016-03-01
This study establishes a new framework which combines the recursive model with the Fractionally Integrated Vector Error Correction Model (FIVECM) to investigate the cointegration relationship among 9 securitized real estate indices, which are divided into three groups: Asian, European and North American groups. Our new combined framework has the advantage of reflecting the changes in cointegration dynamics over a period of time instead of a single result for the whole period. The results show that the three groups of markets follow a similar cointegration trend: the cointegration relationship gradually increases before the global financial crisis, reaches a peak during the crisis, and dies down gradually after the crisis. However, cointegration among Asian and European countries occurs at a much later time than cointegration among North American countries does, showing that North America is the source of cointegration, while Asia and Europe are the recipients. This study has important implications to investors and related authorities that investors can adjust their portfolio according to the test results to reduce their risk, while related authorities can take appropriate measures to stabilize the economy and mitigate the effects of financial crises.
On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms
1976-08-01
a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, R.N.J.; Vries, L.B.
1986-01-01
The authors present an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, Raymond N.J.; Vries, Lodewijk B.
1986-01-01
This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
International Nuclear Information System (INIS)
Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier
2015-01-01
Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling
Directory of Open Access Journals (Sweden)
Ernest Kissi
2018-03-01
Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.
On the non-existence of a Bartlett correction for unit root tests
DEFF Research Database (Denmark)
Jensen, Jens Ledet; Wood, Andrew T.A.
1997-01-01
There has been considerable recent interest in testing for a unit root in autoregressive models, especially in the context of cointegration models in econometrics. The likelihood ratio test for a unit root has non-standard asymptotic behaviour. In particular, when the errors are Gaussian, the lim...... for improved distributional approximations, and the question of whether W admits a Bartlett correction is of interest. In this note we establish that a Bartlett correction does not exist in the simplest unit root model. © 1997 Elsevier Science B.V....
The long and short run forcing variables of purchasing power parity of ASEAN-5
Abdalrahman AbuDalu; Elsadig Musa Ahmed
2013-01-01
This study examines the long-run and short-run forcing variables of purchasing power parity (PPP) for ASEAN-5 currencies vis-a-vis the U.S. dollar, i.e., their real effective exchange rate (REER). This study uses the autoregressive distributed lag (ARDL) approach to co-integration over the period 1991:Q1 – 2006:Q2. Our empirical results suggest that the domestic money supply (M1) is a significant long run forcing variable for the REERs of Malaysia, Indonesia, the Philippines, and Singapore. H...
Directory of Open Access Journals (Sweden)
Adwin Surja Atmadja
2009-01-01
Full Text Available This study attempts to examine the existence of cointegration relationship and the short run dynamic interaction among the five ASEAN stock market indices in the period of before and during the 2007 financial crisis. The multivariate time series analysis frameworks are employed to the series in both sub-sample periods in order to answer the hypotheses.The study finds two cointegrating vectors in the series before the financial crisis period, however it fails to detect any cointegrating vector in the period of financial crisis. Granger causality tests applied to the series reveal that number of significant causal linkages between two variables increase during the crisis period. Moreover, the accounting innovation analysis shows an increase in the explanatory power of an endogenous variable to another within the system during the crisis period, indicating that the contagious effect of the 2007-US financial crisis has entered into the ASEAN capital market, and significantly influenced the regional indices’ movements.
INTEGRASI PASAR SAHAM ASEAN-5: ANALISIS SEBELUM DAN SEPANJANG KRISIS KEUANGAN GLOBAL 2007-2008
Directory of Open Access Journals (Sweden)
Endri Endri
2017-03-01
Full Text Available This article investigated both the static and dynamic inter dependence of the fivestock markets in the original Association of Southeast Asian Nations countries (ASEAN-5,namely Indonesia, Singapore, Malaysia, Thailand and Philippine. Using data from 2000-2008,the paper employed both correlation and co-integration analysis to describe the behavior ofthe above markets, both before and during 2007-2008 Global financial crisis. Examination ofstock market index, using correlation analysis revealed an increase in the interdependencies(increased correlation across the Southeast Asian stock markets during the crisis. Multivari-ate co-integration tests showed that ASEAN-5 stock markets only had one significant co inte-gration vector along the crisis period. Along the full period there was one vector that signifi-cantly integrated or five common trends. This finding indicated the long time co-integrationamong the ASEAN-5 stock markets. On the other hand, along the global financial crisis noproof of long time co-integration was found among the ASEAN-5.
Directory of Open Access Journals (Sweden)
Weide Li
2017-01-01
Full Text Available Air pollution in China is becoming more serious especially for the particular matter (PM because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM, in which the parameters c and g are optimized by flower pollination algorithm (FPA. Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.
Directory of Open Access Journals (Sweden)
Luis Gonzaga Baca Ruiz
2016-08-01
Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.
Energy Technology Data Exchange (ETDEWEB)
Cabral, Renata [Universidade de Sao Paulo (USP), SP (Brazil). Faculdade de Economia e Administracao; Parente, Virginia [Universidade de Sao Paulo (USP), SP (Brazil). Inst. de Eletrotecnica e Energia
2008-07-01
The purpose of the present study is to estimate the long-run elasticities - manly price and income - of the demand for gas natural in the industrial category. After determining that the series under study were non-stationary, we chose to use the cointegration approach, estimating a Vector Error Correction Model (VEC Model). The obtained results show that the price elasticity for industrial sector in Brazil is higher than income elasticity. Although both of then is near to one, the price elasticity is higher that one unit while income elasticity is slightly lower. Predictions for the gas natural consumption in Brazil for industrials for 2008-2012 period are also made. (author)
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...
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.
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Hofman, Radek
2012-01-01
Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf
ANALYSIS OF SUPPLY RESPONSE AND PRICE RISK ON RICE PRODUCTION IN NIGERIA
Directory of Open Access Journals (Sweden)
Opeyemi Eyitayo Ayinde
2017-03-01
Full Text Available Nigeria, like most African countries, has engaged in agricultural liberalization since 1986 in the hope that reforms emphasizing price incentives will encourage producers to respond. Thus far, the reforms seem to have introduced greater uncertainty into the market given increasing rates of price volatility. This study amongst other things therefore seeks to determine and model the responsiveness of rice supply to price risk in Nigeria. Statistical information on domestic and imported quantities of rice was obtained for 41 years (1970 to 2011 from various sources, such as the Food and Agriculture Organization (FAO database, Federal Ministry of Agriculture statistical bulletins, Central Bank of Nigeria statistical bulletins and National Bureau of Statistic (NBS. Data were analyzed using equilibrium output supply function, co-integration models, and vector autoregressive distributed lag model. Rice importation was statistically significant and changes in output were also responsive to changes in price. The results indicate that producers are more responsive not only to price and non-price factor but also to price risk and exchange rate. It is therefore imperative to reduce the effects of price risk as to increase the response of producer to supply by bridging the gap in production
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.
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.
A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships
Directory of Open Access Journals (Sweden)
Shuang Guan
2017-10-01
Full Text Available Many of the existing autoregressive moving average (ARMA forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs for a two-factor first-order autoregressive (AR(1 model and forecasting the training data with the AR(1 model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m model. Lastly, we forecasted test data with the ARMA(1,m model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI from 2001 to 2015 and the international gold price from 2000 to 2010.
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
Forecasting systems reliability based on support vector regression with genetic algorithms
International Nuclear Information System (INIS)
Chen, K.-Y.
2007-01-01
This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error
Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model
Wang, Qijie
2015-08-01
The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.
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)
Is job insecurity a driver of the housing cycle? Some evidence in the Spanish case
Directory of Open Access Journals (Sweden)
Arestis Philip
2015-01-01
Full Text Available The aim of this paper is to study a possible connection between the characteristics of the labour market, namely, employment protection and flexibility, and the dynamics of the housing market. More specifically, this contribution analyses whether the poor quality of employment in a given economy could cause the collapse of the housing market and impairs its recovery. In the second stage of our analysis, we provide estimates for Spain over the period 1984-2014. In doing so, the autoregressive distributed lag (ARDL bounds test for cointegration is employed.
Testing the Conditional Mean Function of Autoregressive Conditional Duration Models
DEFF Research Database (Denmark)
Hautsch, Nikolaus
be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor......This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors...
International Nuclear Information System (INIS)
Benth, Fred Espen; Taib, Che Mohd Imran Che
2013-01-01
We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
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)
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M
Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes
A Smooth Transition Logit Model of the Effects of Deregulation in the Electricity Market
DEFF Research Database (Denmark)
Hurn, A.S.; Silvennoinen, Annastiina; Teräsvirta, Timo
We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting of specific......We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting...... of specification, including testing linearity, estimation and evaluation of these models is constructed. Nonlinear least squares estimation of the parameters of the model is discussed. Evaluation by misspecification tests is carried out using tests derived in a companion paper. The use of the modelling strategy...
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan
2017-11-20
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan; Genton, Marc G.
2017-01-01
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Directory of Open Access Journals (Sweden)
E. Pishbahar
2015-05-01
Full Text Available There are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. To answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. In the present study, the Bayesian Vector autoregresive model and seasonality data for the years between 1991 and 2013 was used to determine the impact of monetary policy on value-added agriculture. Predicts of Vector autoregresive model are usually divertaed due to a lot of parameters in the model. Bayesian vector autoregresive model estimates more reliable predictions due to reducing the number of included parametrs and considering the former models. Compared to the Vector Autoregressive model, the coefficients are estimated more accurately. Based on the results of RMSE in this study, previous function Nrmal-Vyshart was identified as a suitable previous disteribution. According to the results of the impulse response function, the sudden effects of shocks in macroeconomic variables on the value added in agriculture and domestic venture capital are stable. The effects on the exchange rates, tax revenues and monetary will bemoderated after 7, 5 and 4periods. Monetary policy shocks ,in the first half of the year, increased the value added of agriculture, while in the second half of the year had a depressing effect on the value added.
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...
Asymptotically stable phase synchronization revealed by autoregressive circle maps
Drepper, F. R.
2000-11-01
A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.
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...
An introduction to vectors, vector operators and vector analysis
Joag, Pramod S
2016-01-01
Ideal for undergraduate and graduate students of science and engineering, this book covers fundamental concepts of vectors and their applications in a single volume. The first unit deals with basic formulation, both conceptual and theoretical. It discusses applications of algebraic operations, Levi-Civita notation, and curvilinear coordinate systems like spherical polar and parabolic systems and structures, and analytical geometry of curves and surfaces. The second unit delves into the algebra of operators and their types and also explains the equivalence between the algebra of vector operators and the algebra of matrices. Formulation of eigen vectors and eigen values of a linear vector operator are elaborated using vector algebra. The third unit deals with vector analysis, discussing vector valued functions of a scalar variable and functions of vector argument (both scalar valued and vector valued), thus covering both the scalar vector fields and vector integration.
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.
Product development projects dynamics and emergent complexity
Schlick, Christopher
2016-01-01
This book primarily explores two topics: the representation of simultaneous, cooperative work processes in product development projects with the help of statistical models, and the assessment of their emergent complexity using a metric from theoretical physics (Effective Measure Complexity, EMC). It is intended to promote more effective management of development projects by shifting the focus from the structural complexity of the product being developed to the dynamic complexity of the development processes involved. The book is divided into four main parts, the first of which provides an introduction to vector autoregression models, periodic vector autoregression models and linear dynamical systems for modeling cooperative work in product development projects. The second part presents theoretical approaches for assessing complexity in the product development environment, while the third highlights and explains closed-form solutions for the complexity metric EMC for vector autoregression models and linear dyn...
Bivariate Cointegration Analysis of Energy-Economy Interactions in Iran
Directory of Open Access Journals (Sweden)
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.
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)
Robust estimation of autoregressive processes using a mixture-based filter-bank
Czech Academy of Sciences Publication Activity Database
Šmídl, V.; Anthony, Q.; Kárný, Miroslav; Guy, Tatiana Valentine
2005-01-01
Roč. 54, č. 4 (2005), s. 315-323 ISSN 0167-6911 R&D Projects: GA AV ČR IBS1075351; GA ČR GA102/03/0049; GA ČR GP102/03/P010; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian estimation * probabilistic mixtures * recursive estimation Subject RIV: BC - Control Systems Theory Impact factor: 1.239, year: 2005 http://library.utia.cas.cz/separaty/historie/karny-robust estimation of autoregressive processes using a mixture-based filter- bank .pdf
Simulation of demand (or consumption) of fuel and lubricating oils in countries of Asia
International Nuclear Information System (INIS)
Tuzelbaev, B.I.; Khisarov, B.D.
1997-01-01
Demand on fuel and lubricating oils mixtures is estimated in transport sector of the Asia countries. Model of demand is constructed with help of co-integration structures and error correction model for definition of fuel and lubricating oils mixtures elasticity. Researches have been conducted with use of program complex in operational medium of the Windows-95. Co-integrating vectors for demand on gasoline were defined by all of considered countries (beside Thailand), for demand on diesel fuel - for Indonesia and South Korea
Determinants of External Debt: The Case of Malaysia
Rafik, Rabiatul Adawiyah Mohamed
2015-01-01
Over the past 30 years, Malaysia‘s external debt has been on an increase, with the increase closely linked to a number of economic factors. The changing quantities and qualities of external debt have become a national concern. Data related to the changes in dependent and independent variables between 1970 and 2013 was used in the study. The model was tested for unit root tests, cointegration test, vector error correction model, and Granger causality test. The cointegration test indicates ther...
Long run economic growth and tourism: Inferring from Uruguay
Brida, Juan Gabriel; Lionetti, Stefania; Risso, Wiston Adrian
2009-01-01
Argentina is the principal source of tourism in Uruguay. This paper analyzes the effects in the long run of tourism from Argentina on the economic growth of Uruguay. Using quarterly data from 1987.I to 2006.IV, the study uses co-integration analysis and shows the existence of one cointegrated vector among Uruguayan real per capita GDP, Argentinean tourism expenditure, and real exchange rate between Uruguay and Argentina, and tests that the causality relationship positively goes in one way fro...
Price Conduction Mechanism of Chinaâ€™s Wheat Industry Chain Based on VECM
ZHU, Haiyan
2015-01-01
With the aid of the VECM (vector error correction model), this paper studied dynamic effect of wheat price and flour price conduction mechanism in the wheat industry chain. Study results indicate that in a long term, wheat price and flour price have equilibrium relationship. Through threshold co-integration test, it found that there is no threshold co-integration relationship between wheat price and flour price. This can be adjusted using the linear error correction mode (LECM). In a short te...
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 role of natural gas consumption and trade in Tunisia's output
International Nuclear Information System (INIS)
Farhani, Sahbi; Shahbaz, Muhammad; Arouri, Mohamed; Teulon, Frédéric
2014-01-01
This paper examines the impact of natural gas consumption, real gross fixed capital formation and trade on the real GDP in the case of Tunisia over the period 1980–2010. We use an Autoregressive Distributed Lag (ARDL) bounds testing approach to test for cointegration between the variables. The Toda–Yamamoto approach is then used to test for causality. Our findings indicate the existence of a long-term relationship between the variables. Natural gas consumption, real gross fixed capital formation and trade add in economic growth. Natural gas consumption, real gross fixed capital formation and real trade cause real GDP in Tunisia. These findings open up new insights for policymakers to formulate a comprehensive energy policy to sustain economic growth in the long-term. - Highlights: • We study how gas consumption, fixed capital formation and trade affect GDP in Tunisia. • We use auto-regressive distributed lag bounds testing approach and causality tests. • Gas consumption, real gross fixed capital formation and trade add in economic growth
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.
Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.
2017-01-01
Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.
Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman
2017-08-01
Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.
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.
Upport vector machines for nonlinear kernel ARMA system identification.
Martínez-Ramón, Manel; Rojo-Alvarez, José Luis; Camps-Valls, Gustavo; Muñioz-Marí, Jordi; Navia-Vázquez, Angel; Soria-Olivas, Emilio; Figueiras-Vidal, Aníbal R
2006-11-01
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.
Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru
2010-12-01
The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2018-01-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
International Journal of Arts and Humanities(IJAH) Ethiopia
African Journals Online (AJOL)
Nneka Umera-Okeke
test, Johansen Cointegration test; vector error correction model and Granger ..... Estimated regression models indicate that .... Stochastic error term b0. = ... Integrated at order one- commonly denoted 1(1), = 5 are the parameters to be.
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...
Ağaç, Kübra; Koçak, Kasım; Deniz, Ali
2015-04-01
A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built
Short-run and long-run elasticities of import demand for crude oil in Turkey
International Nuclear Information System (INIS)
Altinay, Galip
2007-01-01
The aim of this study is to attempt to estimate the short-run and the long-run elasticities of demand for crude oil in Turkey by the recent autoregressive distributed lag (ARDL) bounds testing approach to cointegration. As a developing country, Turkey meets its growing demand for oil principally by foreign suppliers. Thus, the study focuses on modelling the demand for imported crude oil using annual data covering the period 1980-2005. The bounds test results reveal that a long-run cointegration relationship exists between the crude oil import and the explanatory variables: nominal price and income, but not in the model that includes real price in domestic currency. The long-run parameters are estimated through a long-run static solution of the estimated ARDL model, and then the short-run dynamics are estimated by the error correction model. The estimated models pass the diagnostic tests successfully. The findings reveal that the income and price elasticities of import demand for crude oil are inelastic both in the short run and in the long run
International Nuclear Information System (INIS)
Naser, Hanan
2017-01-01
This study uses Johansen cointegration technique to examine both the equilibrium relationship and the causality between oil consumption, nuclear energy consumption, oil price and economic growth. To do so, four industrialized countries including the USA, Canada, Japan, and France are investigated over the period from 1965 to 2010. The cointegration test results suggest that the proposed variables tend to move together in the long run in all countries. In addition, the causal linkage between the variables is scrutinized through the exogeneity test. The results point that energy consumption (i.e., oil or nuclear) has either a predictive power for economic growth, or feedback impact with real GDP growth in all countries. Results suggest that oil consumption is not only a major factor of economic growth in all the investigated countries, it also has a predictive power for real GDP in the USA, Japan, and France. Precisely, increasing oil consumption by 1% increases the economic growth in Canada by 3.1%., where increasing nuclear energy consumption by 1% in Japan and France increases economic growth by 0.108 and 0.262%, respectively. Regarding nuclear energy consumption-growth nexus, results illustrate that nuclear energy consumption has a predictive power for real economic growth in the USA, Canada, and France. On the basis of speed of adjustment, it is concluded that there is bidirectional causality between oil consumption and economic growth in Canada. On the other hand, there is bidirectional causal relationship between nuclear energy consumption and real GDP growth in Japan. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Naser, Hanan [Arab Open University, Faculty of Business Studies, A' ali (Bahrain)
2017-06-15
This study uses Johansen cointegration technique to examine both the equilibrium relationship and the causality between oil consumption, nuclear energy consumption, oil price and economic growth. To do so, four industrialized countries including the USA, Canada, Japan, and France are investigated over the period from 1965 to 2010. The cointegration test results suggest that the proposed variables tend to move together in the long run in all countries. In addition, the causal linkage between the variables is scrutinized through the exogeneity test. The results point that energy consumption (i.e., oil or nuclear) has either a predictive power for economic growth, or feedback impact with real GDP growth in all countries. Results suggest that oil consumption is not only a major factor of economic growth in all the investigated countries, it also has a predictive power for real GDP in the USA, Japan, and France. Precisely, increasing oil consumption by 1% increases the economic growth in Canada by 3.1%., where increasing nuclear energy consumption by 1% in Japan and France increases economic growth by 0.108 and 0.262%, respectively. Regarding nuclear energy consumption-growth nexus, results illustrate that nuclear energy consumption has a predictive power for real economic growth in the USA, Canada, and France. On the basis of speed of adjustment, it is concluded that there is bidirectional causality between oil consumption and economic growth in Canada. On the other hand, there is bidirectional causal relationship between nuclear energy consumption and real GDP growth in Japan. (orig.)
On the income–nuclear energy–CO2 emissions nexus revisited
International Nuclear Information System (INIS)
Baek, Jungho; Pride, Dominique
2014-01-01
This paper seeks to contribute to the debate over the income–nuclear enery–CO 2 emissions nexus by taking specific account of the possible endogeneity of income, which has been largely ignored by early studies. A multivariate cointegrated vector autoregression (CVAR) is applied to top six nuclear generating countries. We find that nuclear energy tends to reduce CO 2 emission for all countries. It is also found that income has a beneficial effect on the environment only in some countries. Finally, we find that CO 2 emissions and income are indeed determined simultaneously, while nuclear energy acts exogenously, indicating that nuclear energy is the driving variable, which significantly influences the long-run movements of CO 2 emissions and income, but is not affected by CO 2 emissions and income in the model. - Highlights: • We examine the income–nuclear energy–CO 2 emissions nexus in top six nuclear generating countries. • The model pays special attention to the possible endogeneity of income. • Nuclear energy is found to have a beneficial effect on the environment in all countries. • Income has a favorable effect on the environment only in some countries. • CO 2 emissions and income are indeed found to be determined simultaneously
Ethanol, Corn, and Soybean Price Relations in a Volatile Vehicle-Fuels Market
Directory of Open Access Journals (Sweden)
Cesar Escalante
2009-06-01
Full Text Available The rapid upward shift in ethanol demand has raised concerns about ethanol’s impact on the price level and volatility of agricultural commodities. The popular press attributes much of this volatility in commodity prices to a price bubble in ethanol fuel and recent deflation. Market economics predicts not only a softening of demand to high commodity prices but also a positive supply response. This volatility in ethanol and commodity prices are investigated using cointegration, vector error corrections (VECM, and multivariate generalized autoregressive conditional heteroskedascity (MGARCH models. In terms of derived demand theory, results support ethanol and oil demands as derived demands from vehicle-fuel production. Gasoline prices directly influence the prices of ethanol and oil. However, of greater significance for the fuel versus food security issue, results support the effect of agricultural commodity prices as market signals which restore commodity markets to their equilibriums after a demand or supply event (shock. Such shocks may in the short-run increase agricultural commodity prices, but decentralized freely operating markets will mitigate the persistence of these shocks. Results indicate in recent years there are no long-run relations among fuel (ethanol, oil and gasoline prices and agricultural commodity (corn and soybean prices.
Integer valued autoregressive processes with generalized discrete Mittag-Leffler marginals
Directory of Open Access Journals (Sweden)
Kanichukattu K. Jose
2013-05-01
Full Text Available In this paper we consider a generalization of discrete Mittag-Leffler distributions. We introduce and study the properties of a new distribution called geometric generalized discrete Mittag-Leffler distribution. Autoregressive processes with geometric generalized discrete Mittag-Leffler distributions are developed and studied. The distributions are further extended to develop a more general class of geometric generalized discrete semi-Mittag-Leffler distributions. The processes are extended to higher orders also. An application with respect to an empirical data on customer arrivals in a bank counter is also given. Various areas of potential applications like human resource development, insect growth, epidemic modeling, industrial risk modeling, insurance and actuaries, town planning etc are also discussed.
Analysis of nonlinear systems using ARMA [autoregressive moving average] models
International Nuclear Information System (INIS)
Hunter, N.F. Jr.
1990-01-01
While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs
Least squares estimation in a simple random coefficient autoregressive model
DEFF Research Database (Denmark)
Johansen, S; Lange, T
2013-01-01
The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...... we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source...
Autoregressive techniques for acoustic detection of in-sodium water leaks
International Nuclear Information System (INIS)
Hayashi, K.
1997-01-01
We have been applied a background signal whitening filter built by univariate autoregressive model to the estimation problem of the leak start time and duration. In the 1995 present benchmark stage, we evaluated the method using acoustic signals from real hydrogen or water/steam injection experiments. The results show that the signal processing technique using this filter can detect reliability the leak signals with a sufficient signal-to-noise ratio. Even if the sensor signal contains non-boiling or non-leak high-amplitude pulses, they can be classified by spectral information. Especially, the feature signal made from the time-frequency spectrum of the filtered signal is very sensitive and useful. (author). 8 refs, 14 figs, 6 tabs
Vector Nonlinear Time-Series Analysis of Gamma-Ray Burst Datasets on Heterogeneous Clusters
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Ioana Banicescu
2005-01-01
Full Text Available The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for the efficient execution of this type of analysis on heterogeneous clusters. Based on partitioning processors into groups for efficient communications and a dynamic loop scheduling approach for load balancing, the strategy addresses the variability of the computational loads of the datasets, as well as the unpredictable irregularities of the cluster environment. Results from preliminary tests of using this strategy to fit gamma-ray burst time profiles with vector functional coefficient autoregressive models on 64 processors of a general purpose Linux cluster demonstrate the effectiveness of the strategy.
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)
Nonlinear structural damage detection using support vector machines
Xiao, Li; Qu, Wenzhong
2012-04-01
An actual structure including connections and interfaces may exist nonlinear. Because of many complicated problems about nonlinear structural health monitoring (SHM), relatively little progress have been made in this aspect. Statistical pattern recognition techniques have been demonstrated to be competitive with other methods when applied to real engineering datasets. When a structure existing 'breathing' cracks that open and close under operational loading may cause a linear structural system to respond to its operational and environmental loads in a nonlinear manner nonlinear. In this paper, a vibration-based structural health monitoring when the structure exists cracks is investigated with autoregressive support vector machine (AR-SVM). Vibration experiments are carried out with a model frame. Time-series data in different cases such as: initial linear structure; linear structure with mass changed; nonlinear structure; nonlinear structure with mass changed are acquired.AR model of acceleration time-series is established, and different kernel function types and corresponding parameters are chosen and compared, which can more accurate, more effectively locate the damage. Different cases damaged states and different damage positions have been recognized successfully. AR-SVM method for the insufficient training samples is proved to be practical and efficient on structure nonlinear damage detection.
Directory of Open Access Journals (Sweden)
Rahul Tripathi
2014-01-01
Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.
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.
Colombian equity return and narrow money supply: an asymmetric cointegration analysis
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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.
Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model
Vazifedan, Turaj; Shitan, Mahendran
Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.
The Disparate Labor Market Impacts of Monetary Policy
Carpenter, Seth B.; Rodgers, William M., III
2004-01-01
Employing two widely used approaches to identify the effects of monetary policy, this paper explores the differential impact of policy on the labor market outcomes of teenagers, minorities, out-of-school youth, and less-skilled individuals. Evidence from recursive vector autoregressions and autoregressive distributed lag models that use…
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.
Directory of Open Access Journals (Sweden)
Sun Zhangzhen
2012-08-01
Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.
1987-02-04
U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.
2016-01-01
Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing
DEFF Research Database (Denmark)
Meng, Anders; Shawe-Taylor, John
2005-01-01
In music genre classification the decision time is typically of the order of several seconds however most automatic music genre classification systems focus on short time features derived from 10-50ms. This work investigates two models, the multivariate gaussian model and the multivariate...... probability kernel. In order to examine the different methods an 11 genre music setup was utilized. In this setup the Mel Frequency Cepstral Coefficients (MFCC) were used as short time features. The accuracy of the best performing model on this data set was 44% as compared to a human performance of 52...... autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product...
Projecting county pulpwood production with historical production and macro-economic variables
Consuelo Brandeis; Dayton M. Lambert
2014-01-01
We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory
DEFF Research Database (Denmark)
Nonejad, Nima
We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte...... Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive...... forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications....
Failure and reliability prediction by support vector machines regression of time series data
International Nuclear Information System (INIS)
Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique
2011-01-01
Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.
The Impact of Financial Development on Economic Growth in Nigeria: An ARDL Analysis
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Eugene Iheanacho
2016-11-01
Full Text Available This study empirically examines the relationship between financial intermediary development and economic growth in Nigeria over the period 1981–2011 using the auto-regressive distributed lag (ARDL approach to co-integration analysis. The results show that the relationship between financial development and economic growth in Nigeria is not significantly different from what has been observed generally in oil-dependent economies. The relationship between financial intermediary development and economic growth in Nigeria is found to be insignificantly negative in the long-run and significantly negative in the short-run. The results highlight the dominant role of the oil sector in economic activities in Nigeria.
An empirical analysis of energy demand in Namibia
International Nuclear Information System (INIS)
De Vita, G.; Hunt, L.C.
2006-01-01
Using a unique database of end-user local energy data and the recently developed Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration, we estimate the long-run elasticities of the Namibian energy demand function at both aggregated level and by type of energy (electricity, petrol and diesel) for the period 1980-2002. Our main results show that energy consumption responds positively to changes in GDP and negatively to changes in energy price and air temperature. The differences in price elasticities across fuels uncovered by this study have significant implications for energy taxation by Namibian policy makers. We do not find any significant cross-price elasticities between different fuel types. (author)
The Determinants of Stock Market Returns: An ARDL Investigation on Borsa Istanbul
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Sevinç Güler
2014-03-01
Full Text Available In this paper we examine the long run and the short run dynamics of stock return and macroeconomic and financial variables like gold prices, oil prices, export volume, import volume and exchange rate. The empirical investigation employed on monthly data between January 1988 to November 2013. The Autoregressive Distrubuted Lag (ARDL called analytical-cointegration technique is applied to capture the dynamics of short-run and long-run relationship between veriables. According to results we found a long run relationship between stock return and economic factors and existence of significant relationship between import and stock return in long run and short run models
Molenaar, Peter C M
2017-01-01
Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.
Bušs, Ginters
2009-01-01
Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to varia...
Energy consumption and economic growth in China: A multivariate causality test
International Nuclear Information System (INIS)
Wang Yuan; Wang Yichen; Zhou Jing; Zhu Xiaodong; Lu Genfa
2011-01-01
This study takes a fresh look at the direction of causality between energy consumption and economic growth in China during the period from 1972 to 2006, using a multivariate cointegration approach. Given the weakness associated with the bivariate causality framework, the current study performs a multivariate causality framework by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory. Using the recently developed autoregressive distributed lag (ARDL) bounds testing approach, a long-run equilibrium cointegration relationship has been found to exist between economic growth and the explanatory variables: energy consumption, capital and employment. Empirical results reveal that the long-run parameter of energy consumption on economic growth in China is approximately 0.15, through a long-run static solution of the estimated ARDL model, and that for the short-run is approximately 0.12 by the error correction model. The study also indicates the existence of short-run and long-run causality running from energy consumption, capital and employment to economic growth. The estimation results imply that energy serves as an important source of economic growth, thus more vigorous energy use and economic development strategies should be adopted for China. - Highlights: → Cointegration is only present when real GDP is the dependent variable. →The long-run causality running from energy consumption to economic growth. →China is an energy dependent economy.
Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika
2017-06-01
Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.
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.
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
On the Feed-back Mechanism of Chinese Stock Markets
Lu, Shu Quan; Ito, Takao; Zhang, Jianbo
Feed-back models in the stock markets research imply an adjustment process toward investors' expectation for current information and past experiences. Error-correction and cointegration are often used to evaluate the long-run relation. The Efficient Capital Market Hypothesis, which had ignored the effect of the accumulation of information, cannot explain some anomalies such as bubbles and partial predictability in the stock markets. In order to investigate the feed-back mechanism and to determine an effective model, we use daily data of the stock index of two Chinese stock markets with the expectational model, which is one kind of geometric lag models. Tests and estimations of error-correction show that long-run equilibrium seems to be seldom achieved in Chinese stock markets. Our result clearly shows the common coefficient of expectations and fourth-order autoregressive disturbance exist in the two Chinese stock markets. Furthermore, we find the same coefficient of expectations has an autoregressive effect on disturbances in the two Chinese stock markets. Therefore the presence of such feed-back is also supported in Chinese stock markets.