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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," CORE Discussion Papers RP 1918, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvrp:1918
    Note: In : Econometric Reviews, 26(2-4), 469-486, 2007
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    File URL: http://dx.doi.org/10.1080/07474930701220634
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    References listed on IDEAS

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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.
    2. Lee, Lung-Fei, 1997. "A smooth likelihood simulator for dynamic disequilibrium models," Journal of Econometrics, Elsevier, vol. 78(2), pages 257-294, June.
    3. Maddala, G S & Nelson, Forrest D, 1974. "Maximum Likelihood Methods for Models of Markets in Disequilibrium," Econometrica, Econometric Society, vol. 42(6), pages 1013-1030, November.
    4. Sylvanus Ikhide, 2003. "Was There a Credit Crunch in Namibia Between 1996-2000?," Journal of Applied Economics, Universidad del CEMA, vol. 6, pages 269-290, November.
    5. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    6. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    7. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
    8. Sneessens, Henri R., 1985. "Two alternative stochastic specification and estimation methods for quantity rationing models : A Monte-Carlo comparison," European Economic Review, Elsevier, vol. 29(1), pages 111-136.
    9. Christophe Hurlin & Rafal Kierzenkowski, 2002. "A Theoretical and Empirical Assessment of the Bank Lending Channel and Loan Market Disequilibrium in Poland," NBP Working Papers 22, Narodowy Bank Polski, Economic Research Department.
    10. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    11. Ginsburgh, Victor & Tishler, Asher & Zang, Israel, 1980. "Alternative estimation methods for two-regime models : A mathematical programming approach," European Economic Review, Elsevier, vol. 13(2), pages 207-228, March.
    12. Laroque, Guy & Salanie, B, 1993. "Simulation-Based Estimation of Models with Lagged Latent Variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 119-133, Suppl. De.
    13. Berg, Andreas & Meyer, Renate & Yu, Jun, 2004. "Deviance Information Criterion for Comparing Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 107-120, January.
    14. Kim, Hyun E., 1999. "Was the credit channel a key monetary transmission mechanism following the recent financial crisis in the Republic of Korea?," Policy Research Working Paper Series 2103, The World Bank.
    15. Adolfo Barajas & Roberto Steiner, 2002. "Credit Stagnation in Latin America," IMF Working Papers 02/53, International Monetary Fund.
    16. James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
    17. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
    18. Laffont, Jean-Jacques & Garcia, Rene, 1977. "Disequilibrium Econometrics for Business Loans," Econometrica, Econometric Society, vol. 45(5), pages 1187-1204, July.
    19. Aurora Manrique & Neil Shephard, 1998. "Simulation-based likelihood inference for limited dependent processes," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 174-202.
    20. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
    21. Lee, Lung-fei, 1999. "Estimation of dynamic and ARCH Tobit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 355-390, October.
    22. Dagenais, Marcel G., 1982. "The Tobit model with serial correlation," Economics Letters, Elsevier, vol. 10(3-4), pages 263-267.
    23. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
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    Citations

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    Cited by:

    1. Claessens, Stijn & Sakho, Yaye Seynabou, 2013. "Assessing firms'financing constraints in Brazil," Policy Research Working Paper Series 6624, The World Bank.
    2. repec:zbw:rwirep:0361 is not listed on IDEAS
    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. 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.
    5. 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, Chair for Monetary Policy and International Economics.
    6. 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.
    7. Vouldis, Angelos, 2015. "Credit market disequilibrium in Greece (2003-2011) - a Bayesian approach," Working Paper Series 1805, European Central Bank.

    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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