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Posterior Simulation and Bayes Factors in Panel Count Data Models


  • Siddhartha Chib

    (Washington University)

  • Edward Greenberg

    (Washington University)

  • Rainer Winkelmann

    (University of Canterbury)


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.

Suggested Citation

  • Siddhartha Chib & Edward Greenberg & Rainer Winkelmann, 1996. "Posterior Simulation and Bayes Factors in Panel Count Data Models," Econometrics 9608003, EconWPA, revised 25 Nov 1996.
  • Handle: RePEc:wpa:wuwpem:9608003 Note: Type of Document - ; to print on PostScript; pages: 27

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    References listed on IDEAS

    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995. "Dynamic Count Data Models of Technological Innovation," Economic Journal, Royal Economic Society, vol. 105(429), pages 333-344, March.
    3. 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.
    4. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", pages 125-132.
    5. 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 909-938, July.
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    1. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, pages 282-298.
    2. Du Juan, 2012. "Formal and Informal Care: An Empirical Bayesian Analysis Using the Two-part Model," Forum for Health Economics & Policy, De Gruyter, vol. 15(2), pages 1-42, November.
    3. Tong Li & Xiaoyong Zheng, 2009. "Entry and Competition Effects in First-Price Auctions: Theory and Evidence from Procurement Auctions," Review of Economic Studies, Oxford University Press, vol. 76(4), pages 1397-1429.
    4. Daniel A. Griffith & Manfred M. Fischer & James LeSage, 2017. "The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 75-86, March.
    5. 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.
    6. Perrakis, Konstantinos & Ntzoufras, Ioannis & Tsionas, Efthymios G., 2014. "On the use of marginal posteriors in marginal likelihood estimation via importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 54-69.
    7. 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.
    8. 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.
    9. Klaus Moeltner & James J. Murphy & John K. Stranlund & Maria Alejandra Velez, 2013. "Institutional heterogeneity in social dilemma games: a Bayesian examination," Chapters,in: Handbook on Experimental Economics and the Environment, chapter 2, pages 67-88 Edward Elgar Publishing.
    10. Chib, Siddhartha, 2004. "Markov Chain Monte Carlo Technology," Papers 2004,22, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    11. Hruschka, Harald, 2010. "Considering endogeneity for optimal catalog allocation in direct marketing," European Journal of Operational Research, Elsevier, vol. 206(1), pages 239-247, October.
    12. 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.
    13. Castledine, A. & Moeltner, K. & Price, M.K. & Stoddard, S., 2014. "Free to choose: Promoting conservation by relaxing outdoor watering restrictions," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 324-343.
    14. Davis, Alison & 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, Cambridge University Press, pages 56-74.
    15. Hübler, Olaf, 2005. "Panel Data Econometrics: Modelling and Estimation," Hannover Economic Papers (HEP) dp-319, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    16. 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.
    17. 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.
    18. 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.
    19. Siddhartha Chib & Michael Dueker & Anatoliy Belaygorod, 2005. "Structural Breaks in Estimated DSGE Models with Indeterminacy," Computing in Economics and Finance 2005 357, Society for Computational Economics.
    20. 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.
    21. Wong, Timothy, 2014. "Lights, camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 165-182.

    More about this item


    Bayes factor; Count data; Gibbs sampling; Importance sampling; Marginal likelihood; Metropolis-Hastings algorithm; Markov chain Monte Carlo; Poisson regression.;

    JEL classification:

    • 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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