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Bayesian Inference in Dynamic Disequilibrium Models : an Application to the Polish Credit Market

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  • Luc, BAUWENS

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Department of Economics)

  • Michel, LUBRANO

Abstract

We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the difficulties of stimulating 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.

Suggested Citation

  • Luc, BAUWENS & Michel, LUBRANO, 2006. "Bayesian Inference in Dynamic Disequilibrium Models : an Application to the Polish Credit Market," Discussion Papers (ECON - Département des Sciences Economiques) 2006027, Université catholique de Louvain, Département des Sciences Economiques.
    • Handle: RePEc:ctl:louvec:2006027
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    File URL: http://sites.uclouvain.be/econ/DP/IRES/2006-27.pdf
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    References listed on IDEAS

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    2. Karmelavičius, Jaunius & Mikaliūnaitė-Jouvanceau, Ieva & Petrokaitė, Austėja Petrokaitė, 2022. "Housing and credit misalignments in a two-market disequilibrium framework," ESRB Working Paper Series 135, European Systemic Risk Board.
    3. Torsten Schmidt & Lina Zwick, 2012. "In Search for a Credit Crunch in Germany," Ruhr Economic Papers 0361, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    4. Baird, Matthew & Daugherty, Lindsay & Kumar, Krishna B., 2019. "Improving Estimation of Labor Market Disequilibrium Using Shortage Indicators, with an Application to the Market for Anesthesiologists," IZA Discussion Papers 12129, Institute of Labor Economics (IZA).
    5. Matthew Baird & Lindsay Daugherty & Krishna Kumar, 2017. "Improving Estimation of Labor Market Disequilibrium through Inclusion of Shortage Indicators," CINCH Working Paper Series 1701, Universitaet Duisburg-Essen, Competent in Competition and Health.
    6. Vouldis, Angelos, 2015. "Credit market disequilibrium in Greece (2003-2011) - a Bayesian approach," Working Paper Series 1805, European Central Bank.
    7. Paolo Del Giovane & Andrea Nobili & Federico M. Signoretti, 2017. "Assessing the Sources of Credit Supply Tightening: Was the Sovereign Debt Crisis Different from Lehman?," International Journal of Central Banking, International Journal of Central Banking, vol. 13(2), pages 197-234, June.
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    9. Aloysius Deno Hervino, 2011. "Avoiding risk in working capital credit distribution in Indonesia," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 3(2), pages 199-210, April.
    10. Carpenter, Seth & Demiralp, Selva & Eisenschmidt, Jens, 2014. "The effectiveness of non-standard monetary policy in addressing liquidity risk during the financial crisis: The experiences of the Federal Reserve and the European Central Bank," Journal of Economic Dynamics and Control, Elsevier, vol. 43(C), pages 107-129.
    11. Bofinger, Peter & Maas, Daniel & Ries, Mathias, 2017. "A model of the market for bank credit: The case of Germany," W.E.P. - Würzburg Economic Papers 98, University of Würzburg, Department of Economics.
    12. Tamini, Arnaud & Petey, Joël, 2021. "Hoarding of reserves in the banking industry: Explaining the African paradox," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 214-225.
    13. Schmidt, Torsten & Zwick, Lina, 2012. "In Search for a Credit Crunch in Germany," Ruhr Economic Papers 361, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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    More about this item

    Keywords

    Latent variables; Disequilibrium models; Bayesian inference; Gibbs sampler; Credit rationing;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression 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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