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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 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 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.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2006050.

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Date of creation: 00 Jun 2006
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Handle: RePEc:cor:louvco:2006050

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Related research

Keywords: latent variables; disequilibrium models; Bayesian inference; Gibbs sampler; credit rationing.;

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References

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  1. James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
  2. Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
  3. Laffont, Jean-Jacques & Garcia, Rene, 1977. "Disequilibrium Econometrics for Business Loans," Econometrica, Econometric Society, vol. 45(5), pages 1187-1204, July.
  4. 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.
  5. Aurora Manrique & Neil Shephard, 1998. "Simulation-based likelihood inference for limited dependent processes," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages C174-C202.
  6. 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 S119-33, Suppl. De.
  7. Lee, Lung-Fei, 1997. "A smooth likelihood simulator for dynamic disequilibrium models," Journal of Econometrics, Elsevier, vol. 78(2), pages 257-294, June.
  8. 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 261-78, July.
  9. Dagenais, Marcel G., 1982. "The Tobit model with serial correlation," Economics Letters, Elsevier, vol. 10(3-4), pages 263-267.
  10. 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 107-20, January.
  11. Lee, Lung-fei, 1999. "Estimation of dynamic and ARCH Tobit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 355-390, October.
  12. Christophe Hurlin & Rafal Kierzenkowski, 2002. "A Theoretical and Empirical Assessment of the Bank Lending Channel and Loan Market Disequilibrium in Poland," National Bank of Poland Working Papers 22, National Bank of Poland, Economic Institute.
  13. 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.
  14. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-89, October.
  15. 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.
  16. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
  17. Maddala, G S & Nelson, Forrest D, 1974. "Maximum Likelihood Methods for Models of Markets in Disequilibrium," Econometrica, Econometric Society, vol. 42(6), pages 1013-30, November.
  18. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
  19. Adolfo Barajas & Roberto Steiner, 2002. "Credit Stagnation in Latin America," IMF Working Papers 02/53, International Monetary Fund.
  20. 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.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Claessens, Stijn & Sakho, Yaye Seynabou, 2013. "Assessing firms'financing constraints in Brazil," Policy Research Working Paper Series 6624, The World Bank.

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