de Pooter, Michiel D. Segers, René van Dijk, Herman K.
Year of Publication:
Tinbergen Institute Discussion Paper 06-076/4
Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model and as Hierarchical Linear Mixed Models, the State-Space model and the Panel Data model. We discuss issues involved when drawing Bayesian inference on regression parameters and variance components, in particular when some parameter have substantial posterior probability near the boundary of the parameter region, and show that one should carefully scan the shape of the posterior density function. Analytical, graphical and empirical results are used along the way.
Gibbs sampler MCMC serial correlation non-stationarity reduced rank models state-space models random effects panel data models