dhreg, xtdhreg, and bootdhreg: Commands to implement double-hurdle regression
Christoph Engel () and
Peter Moffatt ()
Stata Journal, 2014, vol. 14, issue 4, 778-797
The dhreg command implements maximum likelihood estimation of the double-hurdle model for continuously distributed outcomes. The command includes the option to fit a p-tobit model, that is, a model that estimates only an intercept for the hurdle equation. The bootdhreg command (the bootstrap version of dhreg) may be convenient if the data-generating process is more complicated or if heteroskedasticity is suspected. The xtdhreg command is a random-effects version of dhreg applicable to panel data. However, this estimator differs from standard random-effects estimators in the sense that the outcome of the first hurdle applies to the complete set of observations for a given subject instead of applying at the level of individual observations. Command options include estimation of a correlation parameter capturing dependence between the two hurdles. Copyright 2014 by StataCorp LP.
Keywords: dhreg; xtdhreg; bootdhreg; hurdle model; double-hurdle model; random-effects double-hurdle model; tobit; p-tobit; inverse Mills ratio; bootstrapping (search for similar items in EconPapers)
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj14-4/st0359/
References: Add references at CitEc
Citations: View citations in EconPapers (26) Track citations by RSS feed
Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0359 link to article purchase
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:14:y:2014:i:4:p:778-797
Ordering information: This journal article can be ordered from
Access Statistics for this article
Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins
More articles in Stata Journal from StataCorp LP
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().