On the Practice of Bayesian Inference in Basic Economic Time Series Models Using Gibbs Sampling

Abstract

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.

Keywords: Gibbs sampler, MCMC, serial correlation, non-stationarity, reduced rank models, state-space models, random effects panel data models

JEL Classification: C11, C15, C22, C23, C30