Bayesian Inference in a Cointegrating Panel Data Model
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.
|Date of creation:||Jul 2007|
|Date of revision:||Jul 2007|
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- Carmen Fernandez & E Ley & Mark F J Steel, 2004.
"Benchmark priors for Bayesian models averaging,"
ESE Discussion Papers
66, Edinburgh School of Economics, University of Edinburgh.
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