IDEAS home Printed from https://ideas.repec.org/p/kyu/dpaper/66.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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

    Download full text from publisher

    File URL: http://www.ip.kyusan-u.ac.jp/keizai-kiyo/dp66.pdf
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    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. 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.
    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. 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.
    5. 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).
    6. 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.
    7. Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, May.
    8. 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.
    9. KaSaundra M. Tomlin, 2000. "The Effects of Model Specification on Foreign Direct Investment Models: An Application of Count Data Models," Southern Economic Journal, John Wiley & Sons, vol. 67(2), pages 460-468, July.
    10. 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(1), pages 1-20, April.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yoshitsugu Kitazawa, 2012. "An improved theoretical ground for the linear feedback model and a new indicator," Discussion Papers 58, Kyushu Sangyo University, Faculty of Economics.
    2. 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.
    3. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    4. Frank Windmeijer, 2006. "GMM for panel count data models," CeMMAP working papers CWP21/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2015. "High dimensional generalized empirical likelihood for moment restrictions with dependent data," Journal of Econometrics, Elsevier, vol. 185(1), pages 283-304.
    6. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    7. Whitney K. Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Yoshitsugu Kitazawa, 2003. "Dynamic Panel Data Model and Moment Generating Function," Discussion Papers 13, Kyushu Sangyo University, Faculty of Economics.
    9. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.
    10. Karsten Schweikert & Manuel Huth & Mark Gius, 2021. "Detecting a copycat effect in school shootings using spatio‐temporal panel count models," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 719-736, October.
    11. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    12. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
    13. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    14. Christian Dustmann & Frank Windmeijer, 2000. "Wages and the demand for health - a life cycle analysis," IFS Working Papers W99/20, Institute for Fiscal Studies.
    15. Stanislav Anatolyev, 2007. "Optimal Instruments In Time Series: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 21(1), pages 143-173, February.
    16. Alain Guay & Florian Pelgrin, 2007. "Using Implied Probabilities to Improve Estimation with Unconditional Moment Restrictions," Cahiers de recherche 0747, CIRPEE.
    17. Yoshitsugu Kitazawa, 2010. "A forward demeaning transformation for a dynamic count panel data model," Discussion Papers 39, Kyushu Sangyo University, Faculty of Economics.
    18. Yoshihide Kakizawa, 2013. "Frequency domain generalized empirical likelihood method," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 691-716, November.
    19. Davis, James C. & Henderson, J. Vernon, 2008. "The agglomeration of headquarters," Regional Science and Urban Economics, Elsevier, vol. 38(5), pages 445-460, September.
    20. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.

    More about this item

    Keywords

    fixed effects zero-inflated Poisson model; predetermined explanatory variables in Poisson part; moment conditions; GMM; EL; 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
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kyu:dpaper:66. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Yoshitsugu Kitazawa (email available below). General contact details of provider: https://edirc.repec.org/data/feksujp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.