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 speciÞcation of the dynamic disequilibrium model which leads to a simple simulation procedure and renders Bayesian inference fully operational. IdentiÞcation issues are discussed. We conduct a speciÞcation 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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Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number
2006050.
Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
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