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Bayesian Inference in a Cointegrating Panel Data Model

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  • Gary Koop
  • Roberto Leon-Gonzalez
  • Rodney Strachan

Abstract

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.

Suggested Citation

  • Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2006. "Bayesian Inference in a Cointegrating Panel Data Model," Discussion Papers in Economics 06/2, Division of Economics, School of Business, University of Leicester.
    • Handle: RePEc:lec:leecon:06/2
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    Cited by:

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    2. Tareq Sadeq, 2008. "Bayesian Analysis of DSGE models: A Panel Approach," Documents de recherche 08-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    3. Michael L. Polemis & Mike G. Tsionas, 2023. "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1602-1621, April.

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    More about this item

    Keywords

    Bayesian; panel data cointegration; error correction model; reduced rank regression; Markov Chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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