Posterior simulation and Bayes factors in panel count data models
AbstractThis 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 86 (1998)
Issue (Month): 1 (June)
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- Siddhartha Chib & Edward Greenberg & Rainer Winkelmann, 1996. "Posterior Simulation and Bayes Factors in Panel Count Data Models," Econometrics 9608003, EconWPA, revised 25 Nov 1996.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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