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MCMC in econometrics

Author

Listed:
  • Dani Gamermam

    (UFRJ)

Abstract

This paper presents the methodology of Markov chain Monte Carlos (MCMC) to statistical inference in Ecometrics. MCMC theory is reviewed and some relevant pratical aspects associated with convergence of the chain are discussed. The most common forms of MCMC using Gibbs sampling and the Metropolis-Hasting algorithm are described. The methods are illustrated in the contexts of time varyng and varyng generalizations of linear regression models. Examples of these models in Econometrics are provided and illustrated with Brazilian economic data.

Suggested Citation

  • Dani Gamermam, 2000. "MCMC in econometrics," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 1(1), pages 7-37, January-J.
    • Handle: RePEc:anp:econom:v:1:y:2000:i:1:p:7-37
    as

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    More about this item

    Keywords

    Bayesian; Dynamic; Hiperparameters; Gibbs Sampling; Markov Chain Monte Carlo; Metropolis-Hastings Algorithm; Spatial Models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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