Posterior Simulation and Bayes Factors in Panel Count Data Models

Siddhartha Chib, Edward Greenberg and Rainer Winkelmann ()
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Siddhartha Chib: Washington University
Edward Greenberg: Washington University

Econometrics from EconWPA

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. Computation of marginal likelihoods and Bayes factors from the simulation output is also considered. The methods are illustrated with several real data applications involving large samples and multiple random effects. This version corrects some typographical errors in the earlier submission.

Keywords: Bayes factor; Count data; Gibbs sampling; Importance sampling; Marginal likelihood; Metropolis-Hastings algorithm; Markov chain Monte Carlo; Poisson regression. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 1996-08-26, Revised 1996-11-25
Note: Type of Document - ; to print on PostScript; pages: 27
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Related works:
Journal Article: Posterior simulation and Bayes factors in panel count data models (1998)
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:9608003

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