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Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market

  • BAUWENS, Luc
  • LUBRANO, Michel

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 speciï¬cation of the dynamic disequilibrium model which leads to a simple simulation procedure and renders Bayesian inference fully operational. Identiï¬cation issues are discussed. We conduct a speciï¬cation 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 RP with number 1918.

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Handle: RePEc:cor:louvrp:1918
Note: In : Econometric Reviews, 26(2-4), 469-486, 2007
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  17. Lee, Lung-Fei, 1997. "A smooth likelihood simulator for dynamic disequilibrium models," Journal of Econometrics, Elsevier, vol. 78(2), pages 257-294, June.
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