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Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes

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  • GORDON, Stephen

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  • BÉLANGER, Gilles

Abstract

This survey provides an introduction to Markov Chain Monte Carlo (MCMC) sampling techniques and to their applications to Bayesian econometrics. In describing the Gibbs sampler and the Metropolis-Hastings algorithm, the emphasis is put on how these techniques can be put into practice; the theoretical foundations are outlined using the elementary properties of Markov chains. To illustrate the potential of MCMC techniques, we decribe several examples where their application has produced clear gains over classical methods of inference. Ce survol fournit une introduction aux techniques d’échantillonnage de type Markov Chain Monte Carlo (MCMC) et leurs applications à l’économétrie bayesienne. Par ce survol notre but n’est pas d’expliquer les fondements théoriques derrière les méthodes de type MCMC, mais bien de faire un exposé pratique des techniques qui s’y rapportent. Nous chercherons surtout à mettre en valeur la facilité et l’étendue des applications par l’utilisation d’exemples simples.

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Paper provided by Université Laval - Département d'économique in its series Cahiers de recherche with number 9509.

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Date of creation: 1995
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Handle: RePEc:lvl:laeccr:9509

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  1. Poirier, Dale J, 1988. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics," Journal of Economic Perspectives, American Economic Association, vol. 2(1), pages 121-44, Winter.
  2. Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
  3. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
  4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  5. John F. Geweke, 1994. "Variable selection and model comparison in regression," Working Papers 539, Federal Reserve Bank of Minneapolis.
  6. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
  7. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Bayesian efficiency analysis with a flexible form: The aim cost function," Discussion Paper 1994-13, Tilburg University, Center for Economic Research.
  8. Peter C.B. Phillips, 1990. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Cowles Foundation Discussion Papers 950, Cowles Foundation for Research in Economics, Yale University.
  9. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
  10. 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.
  11. KOOP, Gary & OSIEWALSKI, Jacek & STEEL, Mark, 1995. "The Components of Output Growth : A Cross-Country Analysis," CORE Discussion Papers 1995003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
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Cited by:
  1. Paquet, Marie-France & Bolduc, Denis, 2004. "Le problème des données longitudinales incomplètes : une nouvelle approche," L'Actualité Economique, Société Canadienne de Science Economique, vol. 80(2), pages 341-361, Juin-Sept.

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