The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Markov Chain Monte Carlo Methods

  • Siddhartha Chib
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2042

Abstract

MCMC methods, an important class of Monte Carlo methods, have played a major role in the growth of Bayesian statistics and econometrics. In an MCMC simulation, one samples a given distribution (say the posterior distribution in a Bayesian model) by simulating a suitably constructed Markov chain whose invariant distribution is the target distribution. The Metropolis–Hastings algorithm and its special case, the Gibbs sampler, are two common ways of devising an MCMC simulation. We discuss how these methods originate, discuss implementation issues and provide examples. The use of MCMC methods in Bayesian prediction and model choice problems is also discussed.

Keywords

Autocorrelation Bayesian econometrics Bayesian prior–posterior analysis Bayesian statistics Invariance Latent variables Marginal likelihood Markov chain Monte Carlo methods Model choice Prediction Proposal densities Reversibility Transition density 
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Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Siddhartha Chib
    • 1
  1. 1.