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Estimation of hurdle models for overdispersed count data

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  • Helmut Farbmacher

    (University of Munich, Germany)

Abstract

Hurdle models based on the zero-truncated Poisson-lognormal distribution are rarely used in applied work, although they incorporate some advantages compared with their negative binomial alternatives. I present a command that enables Stata users to estimate Poisson-lognormal hurdle models. I use adaptive Gauss–Hermite quadrature to approximate the likelihood function, and I evaluate the performance of the estimator in Monte Carlo experiments. The model is applied to the number of doctor visits in a sample of the U.S. Medical Expenditure Panel Survey. Copyright 2011 by StataCorp LP.

Suggested Citation

  • Helmut Farbmacher, 2011. "Estimation of hurdle models for overdispersed count data," Stata Journal, StataCorp LP, vol. 11(1), pages 82-94, March.
    • Handle: RePEc:tsj:stataj:v:11:y:2011:i:1:p:82-94
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    References listed on IDEAS

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