Posterior Simulation and Bayes Factors in Panel Count Data Models
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.
|Date of creation:||26 Aug 1996|
|Date of revision:||25 Nov 1996|
|Note:||Type of Document - ; to print on PostScript; pages: 27|
|Contact details of provider:|| Web page: http://econwpa.repec.org|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Newey, Whitney K & West, Kenneth D, 1987.
"A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,"
Econometric Society, vol. 55(3), pages 703-08, May.
- Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
- Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995.
"Dynamic Count Data Models of Technological Innovation,"
Royal Economic Society, vol. 105(429), pages 333-44, March.
- Richard Blundell & Rachel Griffith & John Van Reenen, 1994. "Dynamic count data models of technological innovation," IFS Working Papers W94/10, Institute for Fiscal Studies.
- Brown, Sarah & Sessions, John G, 1996. " The Economics of Absence: Theory and Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 10(1), pages 23-53, March.
- Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
- Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984.
"Econometric Models for Count Data with an Application to the Patents-R&D Relationship,"
NBER Technical Working Papers
0017, National Bureau of Economic Research, Inc.
- Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-38, July.
When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpem:9608003. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)
If references are entirely missing, you can add them using this form.