Maximum likelihood and two-step estimation of an ordered-probit selection model
Abstract. We discuss the estimation of a regression model with an ordered-probit
selection rule. We have written a Stata command, oheckman, that
computes two-step and full-information maximum-likelihood estimates of this
model. Using Monte Carlo simulations, we compare the performances of these
estimators under various conditions.
View all articles by these authors:
Richard Chiburis, Michael Lokshin
View all articles with these keywords:
oheckman, selection bias, ordered probit, maximum likelihood
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
