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Distribution-Free Estimation of Zero-Inflated Models with Unobserved Heterogeneity

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  • Rodica Gilles
  • Seik Kim

Abstract

Count models often best describe the nature of data in health economics, but the presence of fixed effects with excess zeros and overdispersion strictly limits the choice of estimation methods. This paper presents a quasi-conditional likelihood method to consistently estimate models with excess zeros and unobserved individual heterogeneity when the true generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.

Suggested Citation

  • Rodica Gilles & Seik Kim, 2013. "Distribution-Free Estimation of Zero-Inflated Models with Unobserved Heterogeneity," Working Papers UWEC-2013-03, University of Washington, Department of Economics.
    • Handle: RePEc:udb:wpaper:uwec-2013-03
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    File URL: http://faculty.washington.edu/seikkim/seikkim_qcmle.pdf
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    References listed on IDEAS

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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.
    2. Victoria Moskvina, 2019. "Modelling interregional mobility of university graduates in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 99-122.

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