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GMM versus GQL inferences for panel count data

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  • Jowaheer, Vandna
  • Sutradhar, Brajendra

Abstract

It is well known that the likelihood inferences in dynamic mixed models for count data is extremely complicated. In this paper, we, first, develop a generalized method of moments (GMM) approach for the estimation of the parameters of such models. We then consider an alternative generalized quasi-likelihood (GQL) approach. The relative efficiency of the GQL approach to the GMM approach is examined by comparing the asymptotic variances of the GQL estimates of the parameters to the corresponding asymptotic variances of the GMM estimates.

Suggested Citation

  • Jowaheer, Vandna & Sutradhar, Brajendra, 2009. "GMM versus GQL inferences for panel count data," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1928-1934, September.
  • Handle: RePEc:eee:stapro:v:79:y:2009:i:18:p:1928-1934
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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
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    3. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    4. Imbens, Guido W, 2002. "Generalized Method of Moments and Empirical Likelihood," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 493-506, October.
    5. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    7. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

    1. Bei Wang & Jeffrey R. Wilson, 2018. "Comparative GMM and GQL logistic regression models on hierarchical data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 409-425, February.

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