Estimation of hurdle models for overdispersed count data
Helmut Farbmacher
Department of Economics
University of Munich, Germany
[email protected]
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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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Helmut Farbmacher
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ztpnm, count-data analysis, hurdle models, overdispersion, Poisson-lognormal hurdle models
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