Bayesian Inference in a Cointegrating Panel Data Model

Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan

No 06/2, Discussion Papers in Economics from Division of Economics, School of Business, University of Leicester

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.

Keywords: Bayesian; panel data cointegration; error correction model; reduced rank regression; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C32 C33 (search for similar items in EconPapers)
Date: 2006-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (3)

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Related works:
Chapter: Bayesian inference in a cointegrating panel data model (2008)
Working Paper: Bayesian Inference in a Cointegrating Panel Data Model (2007)
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