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Bayesian inference in a cointegrating panel data model

By Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan


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

Topics: Bayesian, panel data cointegration, error correction model, reduced rank regression, Markov Chain Monte Carlo
Year: 2006
OAI identifier: oai:lra.le.ac.uk:2381/7433

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