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The Stata Journal
Volume 11 Number 1: pp. 82-94



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Estimation of hurdle models for overdispersed count data

Helmut Farbmacher
Department of Economics
University of Munich, Germany
helmut.farbmacher@lrz.uni-muenchen.de
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.
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View all articles with these keywords: ztpnm, count-data analysis, hurdle models, overdispersion, Poisson-lognormal hurdle models

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