Bayesian inference in a cointegrating panel data model

G.M. Koop, Roberto Leon-Gonzalez, Rodney W. Strachan

Research output: Contribution to journalArticle

7 Citations (Scopus)
9 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)433-469
Number of pages36
JournalAdvances in Econometrics
Volume23
DOIs
Publication statusPublished - Jun 2008

Keywords

  • Bayesian
  • panel data cointegration
  • error correction model
  • reduced rank regression
  • Markov Chain
  • Monte Carlo
  • econometrics

Cite this