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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 9608003.
Length: 27 pages
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://220.127.116.11
Bayes factor; Count data; Gibbs sampling; Importance sampling; Marginal likelihood; Metropolis-Hastings algorithm; Markov chain Monte Carlo; Poisson regression.;
Other versions of this item:
- Chib, Siddhartha & Greenberg, Edward & Winkelmann, Rainer, 1998. "Posterior simulation and Bayes factors in panel count data models," Journal of Econometrics, Elsevier, vol. 86(1), pages 33-54, June.
- 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
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.:
- 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.
- Richard Blundell & Rachel Griffith & John Van Reenen, 1994.
"Dynamic count data models of technological innovation,"
IFS Working Papers
W94/10, Institute for Fiscal Studies.
- Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995. "Dynamic Count Data Models of Technological Innovation," Economic Journal, Royal Economic Society, vol. 105(429), pages 333-44, March.
- 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.
- 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.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
- Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
- Tong Li & Xiaoyong Zheng, 2006. "Entry and competition effects in first-price auctions: theory and evidence from procurement auctions," CeMMAP working papers CWP13/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.
- B.P.M. McCabe & G.M. Martin, 2003. "Coherent Predictions of Low Count Time Series," Monash Econometrics and Business Statistics Working Papers 8/03, Monash University, Department of Econometrics and Business Statistics.
- Klaus Moeltner & James J. Murphy & John K. Stranlund & Maria Alejandra Velez, 2007. "Processing Data from Social Dilemma Experiments: A Bayesian Comparison of Parametric Estimators," Working Papers 07-013, University of Nevada, Reno, Department of Economics & University of Nevada, Reno , Department of Resource Economics.
- Davis, Alison F. & Moeltner, Klaus, 2010.
"Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada,"
Agricultural and Resource Economics Review,
Northeastern Agricultural and Resource Economics Association, vol. 39(1), February.
- Allison Davis & Klaus Moeltner, 2009. "Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada," Working Papers 09-001, University of Nevada, Reno, Department of Economics & University of Nevada, Reno , Department of Resource Economics.
- Munkin, Murat K. & Trivedi, Pravin K., 2003. "Bayesian analysis of a self-selection model with multiple outcomes using simulation-based estimation: an application to the demand for healthcare," Journal of Econometrics, Elsevier, vol. 114(2), pages 197-220, June.
- Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
- Li, Tong & Zheng, Xiaoyong, 2012. "Information acquisition and/or bid preparation: A structural analysis of entry and bidding in timber sale auctions," Journal of Econometrics, Elsevier, vol. 168(1), pages 29-46.
- Huang, Ho-Chuan (River), 1999. "Estimation of the SUR Tobit model via the MCECM algorithm," Economics Letters, Elsevier, vol. 64(1), pages 25-30, July.
- Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008.
"Estimating demand systems when outcomes are correlated counts,"
Journal of Econometrics,
Elsevier, vol. 147(2), pages 282-298, December.
- Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin, 2008. "Estimating Demand Systems when Outcomes Are Correlated Count," Staff General Research Papers 12934, Iowa State University, Department of Economics.
- Chib, Siddhartha, 2004. "Markov Chain Monte Carlo Technology," Papers 2004,22, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
- Hübler, Olaf, 2005. "Panel Data Econometrics: Modelling and Estimation," Diskussionspapiere der Wirtschaftswissenschaftlichen FakultÃ¤t der Leibniz UniversitÃ¤t Hannover dp-319, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Munkin, Murat K., 2003. "The MCMC and SML estimation of a self-selection model with two outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 403-424, March.
- Klaus Moeltner & James J. Murphy & John K. Stranlund & Maria Alejandra Velez, 2012. "Institutional Heterogeneity in Social Dilemma Games: A Bayesian Examination," Working Papers 2012-04, University of Alaska Anchorage, Department of Economics.
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.