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Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes

  • Gordon, Stephen

    (Département d’économique, Université Laval)

  • Bélanger, Gilles

    (Département de sciences économiques, Université de Montréal)

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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Article provided by Société Canadienne de Science Economique in its journal L'Actualité économique.

Volume (Year): 72 (1996)
Issue (Month): 1 (mars)
Pages: 27-49

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Handle: RePEc:ris:actuec:v:72:y:1996:i:1:p:27-49
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  1. 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.
  2. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1995. "The components of output growth : A cross-country analysis," Discussion Paper 1995-17, Tilburg University, Center for Economic Research.
  3. Phillips, P C B, 1991. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 333-64, Oct.-Dec..
  4. 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.
  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. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
  7. John F. Geweke, 1994. "Variable selection and model comparison in regression," Working Papers 539, Federal Reserve Bank of Minneapolis.
  8. 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.
  9. 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.
  10. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
  11. 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.
  12. 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.
  13. 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.
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