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Equidispersion and moment conditions for count panel data model

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  • Yoshitsugu Kitazawa

    (Faculty of Economics, Kyushu Sangyo University)

Abstract

This paper proposes some new moment conditions under the assumption of the equidispersion in count panel data model. These are obtained by using the association between variances and covariances in the disturbance. Some Monte Carlo experiments configured for the Poisson model show that the GMM estimators using the new moment conditions perform better than the conventional quasi-differenced GMM estimator and some gains are recognized in using the new moment conditions.

Suggested Citation

  • Yoshitsugu Kitazawa, 2009. "Equidispersion and moment conditions for count panel data model," Discussion Papers 33, Kyushu Sangyo University, Faculty of Economics.
    • Handle: RePEc:kyu:dpaper:33
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    File URL: http://www.ip.kyusan-u.ac.jp/keizai-kiyo/dp33.pdf
    File Function: First version, 2009
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Frank Windmeijer, 2006. "GMM for panel count data models," CeMMAP working papers CWP21/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    4. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    5. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    6. Tony Lancaster, 2002. "Orthogonal Parameters and Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(3), pages 647-666.
    7. 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.
    8. Yoshitsugu Kitazawa, 2007. "Some additional moment conditions for a dynamic count panel data model," Discussion Papers 29, Kyushu Sangyo University, Faculty of Economics, revised Aug 2008.
    9. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-263, May-June.
    10. Richard Blundell & Rachel Griffith & John van Reenen, 1999. "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(3), pages 529-554.
    11. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    12. Chamberlain, Gary, 1992. "Sequential Moment Restrictions in Panel Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 20-26, January.
    13. Windmeijer, Frank, 2000. "Moment conditions for fixed effects count data models with endogenous regressors," Economics Letters, Elsevier, vol. 68(1), pages 21-24, July.
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    Cited by:

    1. Yoshitsugu Kitazawa, 2009. "A negative binomial model and moment conditions for count panel data," Discussion Papers 34, Kyushu Sangyo University, Faculty of Economics.
    2. Yoshitsugu Kitazawa, 2010. "A forward demeaning transformation for a dynamic count panel data model," Discussion Papers 39, Kyushu Sangyo University, Faculty of Economics.

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    More about this item

    Keywords

    count panel data; linear feedback model; equidispersion; implicit operation; crosslinkage moment conditions; GMM; Monte Carlo experiments;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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