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Posterior simulation and Bayes factors in panel count data models


Chib, Siddhartha; Greenberg, Edward; Winkelmann, Rainer (1998). Posterior simulation and Bayes factors in panel count data models. Journal of Econometrics, 86(1):33-54.

Abstract

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a parameterization in common use, and computation of marginal likelihoods and Bayes factors via Chib’s (1995) method is also considered. The methods are illustrated with two real data applications involving large samples and multiple random effects.

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a parameterization in common use, and computation of marginal likelihoods and Bayes factors via Chib’s (1995) method is also considered. The methods are illustrated with two real data applications involving large samples and multiple random effects.

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43 citations in Web of Science®
59 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:1998
Deposited On:11 Feb 2008 12:21
Last Modified:05 Apr 2016 12:17
Publisher:Elsevier
ISSN:0304-4076
Publisher DOI:10.1016/S0304-4076(97)00108-5

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