Crandi

6 Cointegration Rd Springer Com

Leo Migdal
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6 cointegration rd springer com

Part of the book series: Springer Texts in Business and Economics ((STBE)) In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e. the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such a data generating process is a sufficient approximation, so that, in the following, we only consider processes which are integrated of order one (I(1)). This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout

Unable to display preview. Download preview PDF. Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 44)) The particular topics considered in this chapter can be viewed as being at least in part determined by this study’s stated focus on misspecification tests in the context of Ordinary Least Squares. However, the chapter’s subject matter can also be seen to reflect the way in which econometric theory has developed during the past 30 years. In important respects, there has been a broadening in the range of econometric applications, epitomized by such things as the development of microeconometrics so called, the greater use of survey and panel data in...

But notwithstanding this “rippling out,” involving a coincident growing interest in such things as “non-standard” dependent variables, cross-section techniques, and “spatial econometrics” – which has certainly affected the development of econometric software and would... This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Aldrich, J. (1995). Correlations genuine and spurious in Pearson and Yule.

Statistical Science , 10(4), 364–376 Part of the book series: Springer Texts in Business and Economics ((STBE)) In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e. the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such a data generating process is a sufficient approximation, so that, in the following, we only consider processes which are integrated of order one (I(1)). This is a preview of subscription content, log in via an institution to check access.

Tax calculation will be finalised at checkout Unable to display preview. Download preview PDF. Vector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Impulse response functions are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to be traced out in an impulse response analysis have to be specified by imposing appropriate identifying restrictions.

Taking into account the cointegration structure of the variables offers interesting possibilities for imposing identifying restrictions. Therefore VAR models which explicitly take into account the cointegration structure of the variables, so-called vector error correction models, are considered. Specification, estimation and validation of reduced form vector error correction models is briefly outlined and imposing structural short- and long-run restrictions within these models is discussed. I thank an anonymous reader for comments on an earlier draft of this paper that helped me to improve the exposition. This is a preview of subscription content, log in via an institution to check access. Unable to display preview.

Download preview PDF. Unable to display preview. Download preview PDF. This article presents a survey of the analysis of cointegration using the vector autoregressive model. After a few illustrative economic examples, the three model based approaches to the analysis of cointegration are discussed.The vector autoregressive model is defined and the moving average representation of the solution, the Granger representation,... Next the interpretation of the model and its parameters and likelihood based inference follows using reduced rank regression.

The asymptotic analysis includes the distribution of the Gaussian maximum likelihood estimators, the rank test, and test for hypotheses on the cointegrating vectors. Finally, some applications and extensions of the basic model are mentioned and the survey concludes with some open problems. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF. Part of the book series: Springer Texts in Business and Economics ((STBE)) This chapter is addressed to the analysis of cointegrated variables. Properties like superconsistency of the LS estimator and conditions for asymptotic normality are extensively discussed. Error-correction is the reverse of cointegration, which is why we provide an introduction to the analysis of error-correction models as well.

In particular, we discuss cointegration testing. In 2003, Clive W.J. Granger was awarded the Nobel prize for introducing the concept of cointegration. Finally, we stress once more the effect of linear time trends underlying the series. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout

Banerjee, A., Dolado, J. J., & Mestre R. (1998). Error-correction mechanism tests for cointegration in a single-equation framework. Journal of Time Series Analysis, 19, 267–283. In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e.

the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such a data generating process is a sufficient approximation, so that in the following we only consider processes which are integrated of order one (I(1)). This is a preview of subscription content, log in via an institution to check access. Unable to display preview. Download preview PDF. Unable to display preview.

Download preview PDF. Clive W.J. Granger, Some Properties of Time Series Data and their Use in Econometric Model Specification, Journal of Econometrics 16 (1981), pp. 121–130, as well as The concept of co-integration posits that the linear combination of two or more nonstationary time series can be stationary if they are co-integrated (Granger 1981). Co-integration and error correction models are largely applied to solve the spurious regression problem resulting from the inclusion of nonstationary variables in a regression model.

Two main approaches to modeling co-integration are single-equation residual-based and system-based analyses. One precondition for the above tests is that all variables in the co-integration regression should be integrated with the same order. When different orders of integration are identified, alternative tests such as the autoregressive distributed lag bounds test can be applied (Pesaran et al. 2001). Since the mid-1990s, a considerable number of tourism researchers have adopted the co-integration method to address a variety of topics related to tourism demand modeling and forecasting. Numerous efforts have been undertaken to empirically...

This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Part of the book series: The New Palgrave Economics Collection ((NPHE)) Cointegration means that two or more time series share common stochastic trends. Thus, while each series exhibits smooth or trending behaviour, a linear combination of the series exhibits no trend. For example, short-term and long-term interest rates are highly serially correlated (so they are smooth and in this sense exhibit a stochastic trend), but the difference between long rates and short rates — the...

Long rates and short rates are cointegrated. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Unable to display preview. Download preview PDF.

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Part of the book series: Springer Texts in Business and Economics ((STBE)) In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e. the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such a data generating process is a suffic...

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Unable to display preview. Download preview PDF. Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 44)) The particular topics considered in this chapter can be viewed as being at least in part determined by this study’s stated focus on misspecification tests in the context of Ordinary Least Squares. However, the chapter’s subject matter can also be see...

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Statistical Science , 10(4), 364–376 Part Of The Book Series:

Statistical Science , 10(4), 364–376 Part of the book series: Springer Texts in Business and Economics ((STBE)) In the preceding chapter, we used stochastic trends to model nonstationary behaviour of time series, i.e. the variance of the data generating process increases over time, the series exhibits persistent behaviour and its first difference is stationary. For many economic time series, such ...

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Tax calculation will be finalised at checkout Unable to display preview. Download preview PDF. Vector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Impulse response functions are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to ...