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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.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

Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes