Estimation of hurdle models for overdispersed count data
| dc.creator | Farbmacher, Helmut | |
| dc.date | 2017-04-01T17:18:38Z | |
| dc.date.accessioned | 2026-07-09T07:55:45Z | |
| dc.description | 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. | |
| dc.identifier | Other:st0218 | |
| dc.identifier | doi:10.22004/ag.econ.166268 | |
| dc.identifier | https://ageconsearch.umn.edu/record/166268/files/sjart_st0218.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/166268 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/593713 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/166268 | |
| dc.title | Estimation of hurdle models for overdispersed count data | |
| dc.type | Text |