Bayesian Analysis of Dynamic Disequilibrium Models: An Application to the Polish Credit Market

23 Pages Posted: 14 Apr 2005

See all articles by Luc Bauwens

Luc Bauwens

Université catholique de Louvain

Michel Lubrano

Ecole des Hautes Etudes en Sciences Sociales (EHESS)

Date Written: January 14, 2005

Abstract

We show how to perform Bayesian inference in dynamic disequilibrium models by data augmentation. Bayesian inference is much simpler than maximum likelihood estimation since multiple integrals that appear in the likelihood function are avoided by working in the spaces of latent and observed variables. This allows to devise a Gibbs sampler which iterates between the latent variables and the parameters. Identification is discussed. An application to credit rationing is provided involving Polish data.

Keywords: Disequilibrium models, Bayesian inference, Gibbs sampler, credit rationing

JEL Classification: C11, C32, C34, E51

Suggested Citation

Bauwens, Luc and Lubrano, Michel, Bayesian Analysis of Dynamic Disequilibrium Models: An Application to the Polish Credit Market (January 14, 2005). Available at SSRN: https://ssrn.com/abstract=691884 or http://dx.doi.org/10.2139/ssrn.691884

Luc Bauwens (Contact Author)

Université catholique de Louvain ( email )

CORE
34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
32 10 474321 (Phone)
32 10 474301 (Fax)

Michel Lubrano

Ecole des Hautes Etudes en Sciences Sociales (EHESS) ( email )

Greqam, Vieille Charité
2 rue de la Charité
13002 Marseille
France

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