Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market
We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the diffculties 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.
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