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

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  • BAUWENS, Luc
  • LUBRANO, Michel
Abstract
We propose a Bayesian approach for inference in a dynamic disequilibrium model. To circumvent the difficulties raised by the Maddala and Nelson (1974) specification in the dynamic case, we analyze a dynamic extended version of the disequilibrium model of Ginsburgh et al. (1980). We develop a Gibbs sampler based on the simulation of the missing observations. The feasibility of the approach is illustrated by an empirical analysis of the Polish credit market, for which we conduct a specification search using the posterior deviance criterion of Spiegelhalter et al. (2002).
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Suggested Citation

  • BAUWENS, Luc & LUBRANO, Michel, 2007. "Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market," LIDAM Reprints CORE 1918, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1918
    DOI: 10.1080/07474930701220634
    Note: In : Econometric Reviews, 26(2-4), 469-486, 2007
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    1. Shen, Chung-Hua, 2002. "Credit Rationing for Bad Companies in Bad Years: Evidence from Bank Loan Transaction Data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 7(3), pages 261-278, July.
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    7. Ikhide, Sylvanus, 2003. "Was There a Credit Crunch in Namibia Between 1996-2000?," Journal of Applied Economics, Universidad del CEMA, vol. 6(2), pages 1-22, November.
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    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Vouldis, Angelos, 2015. "Credit market disequilibrium in Greece (2003-2011) - a Bayesian approach," Working Paper Series 1805, European Central Bank.

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    More about this item

    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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