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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.

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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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Bibliographic Info

Paper provided by Kyushu Sangyo University, Faculty of Economics in its series Discussion Papers with number 33.

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Length: 21 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:kyu:dpaper:33

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Related research

Keywords: count panel data; linear feedback model; equidispersion; implicit operation; crosslinkage moment conditions; GMM; Monte Carlo experiments;

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References

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  1. Richard Blundell & Rachel Griffith & Frank Windmeijer, 1999. "Individual effects and dynamics in count data models," IFS Working Papers W99/03, Institute for Fiscal Studies.
  2. Lancaster, Tony, 2002. "Orthogonal Parameters and Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 69(3), pages 647-66, July.
  3. 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.
  4. Blundell, Richard & Griffith, Rachel & van Reenen, John, 1999. "Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms," Review of Economic Studies, Wiley Blackwell, vol. 66(3), pages 529-54, July.
  5. Windmeijer, Frank, 2000. "Moment conditions for fixed effects count data models with endogenous regressors," Economics Letters, Elsevier, vol. 68(1), pages 21-24, July.
  6. Frank Windmeijer, 2006. "GMM for panel count data models," CeMMAP working papers CWP21/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  8. 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.
  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 243-63, May-June.
  10. 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.
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Citations

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

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