MCMC in econometrics

Dani Gamermam
Additional contact information
Dani Gamermam: UFRJ

Economia, 2000, vol. 1, issue 1, pages 7-37

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.

Keywords: Bayesian; Dynamic; Hiperparameters; Gibbs Sampling; Markov Chain Monte Carlo; Metropolis-Hastings Algorithm; Spatial Models (search for similar items in EconPapers)
JEL-codes: C15 C11 (search for similar items in EconPapers)
Date: 2000

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Persistent link: http://EconPapers.repec.org/RePEc:anp:econom:v:1:y:2000:i:1:p:7-37

Ordering information: This journal article can be ordered from
Secretaria da ANPEC Rua Tiradentes, 17 - Ingá Niterói, RJ 24210-510 Brazil
http://www.anpec.org.br/revista/

Access Statistics for this article

Economia is edited by Joaquim Pinto de Andrade

More articles in Economia from ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics]
Address: Secretaria da ANPEC Rua Tiradentes, 17 - Ingá Niterói, RJ 24210-510 Brazil
Contact information at EDIRC.
Series data maintained by Hugo E. A. da Gama Cerqueira ().