Bayesian Inference in Dynamic Disequilibrium Models: An Application to the Polish Credit Market
CORE Discussion Paper No. 2006/50
24 Pages Posted: 22 Aug 2006
Date Written: May 2006
We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the difficulties of simulating dynamic latent variables in a Gibbs sampler. We propose an alternative specification of the dynamic disequilibrium model which leads to a simple simulation procedure and renders Bayesian inference fully operational. Identification issues are discussed. We conduct a specification search using the posterior deviance criterion of Spiegelhalter, Best, Carlin, and van der Linde (2002) for a disequilibrium model of the Polish credit market.
Keywords: Latent variables, disequilibrium models, Bayesian inference, Gibbs sampler, credit rationing
JEL Classification: C11, C32, C34, E51
Suggested Citation: Suggested Citation