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Bayesian Inference in a Cointegrating Panel Data Model

  • Gary Koop
  • Roberto Leon-Gonzalez

    ()

  • Rodney Strachan

    ()

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.

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File URL: http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp06-2.pdf
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Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 06/2.

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Date of creation: Jan 2006
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Handle: RePEc:lec:leecon:06/2
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