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Consistent estimation for the full-fledged fixed effects zero-inflated Poisson model

Author

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  • Yoshitsugu Kitazawa

    (Faculty of Economics, Kyushu Sangyo University)

Abstract

This paper advocates the transformations used for the consistent estimation of the full-fledged fixed effects zero-inflated Poisson model whose zero outcomes can arise from both of logit and Poisson parts and which equips both parts with the fixed effects. The valid moment conditions are constructed on the basis of the transformations. The finite sample behaviors of GMM and EL estimators employing the moment conditions are investigated by use of Monte Carlo experiments.

Suggested Citation

  • Yoshitsugu Kitazawa, 2014. "Consistent estimation for the full-fledged fixed effects zero-inflated Poisson model," Discussion Papers 66, Kyushu Sangyo University, Faculty of Economics.
    • Handle: RePEc:kyu:dpaper:66
    as

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    File URL: http://www.ip.kyusan-u.ac.jp/keizai-kiyo/dp66.pdf
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    References listed on IDEAS

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    1. Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, May.
    2. KaSaundra M. Tomlin, 2000. "The Effects of Model Specification on Foreign Direct Investment Models: An Application of Count Data Models," Southern Economic Journal, Southern Economic Association, vol. 67(2), pages 460-468, July.
    3. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    4. Wooldridge, Jeffrey M., 1997. "Multiplicative Panel Data Models Without the Strict Exogeneity Assumption," Econometric Theory, Cambridge University Press, vol. 13(5), pages 667-678, October.
    5. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
    6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    7. Durham, Catherine A. & Pardoe, Iain & Vega-H, Esteban, 2004. "A Methodology for Evaluating How Product Characteristics Impact Choice in Retail Settings with Many Zero Observations: An Application to Restaurant Wine Purchase," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(01), pages 1-20, April.
    8. Ramalho Joaquim J.S., 2005. "Small Sample Bias of Alternative Estimation Methods for Moment Condition Models: Monte Carlo Evidence for Covariance Structures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-20, March.
    9. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    10. Sheng-Pin Hsueh & Wei-Ming Lee, 2012. "A revisit to the relationship between patents and R&D using empirical likelihood estimation," Economics Bulletin, AccessEcon, vol. 32(2), pages 1208-1214.
    11. 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.
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    Cited by:

    1. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.

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    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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