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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Posterior simulation and Bayes factors in panel count data models |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Journal of Econometrics |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0304-4076 |
| Volume | 86 |
| Number | 1 |
| Page Range | 33 - 54 |
| Date | 1998 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1016/S0304-4076(97)00108-5 |
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