Estimation of hurdle models for overdispersed count data

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.


Issue Date:
2011
Publication Type:
Journal Article
DOI and Other Identifiers:
st0218 (Other)
PURL Identifier:
http://purl.umn.edu/166268
Published in:
Stata Journal, Volume 11, Number 1
Page range:
82-94
Total Pages:
13

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 Record created 2017-04-01, last modified 2017-08-22

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