SNP and SML estimation of univariate and bivariate binary–choice models
Abstract.
We discuss the semi-nonparametric approach of Gallant and Nychka (1987,
Econometrica 55: 363–390), the semiparametric maximum likelihood approach
of Klein and Spady (1993, Econometrica 61: 387–421), and a set
of new Stata commands for semiparametric estimation of three binary-choice
models. The first is a univariate model, while the second and the third are
bivariate models without and with sample selection, respectively. The
proposed estimators are √n consistent and asymptotically normal
for the model parameters of interest under weak assumptions on the
distribution of the underlying error terms. Our Monte Carlo simulations
suggest that the efficiency losses of the semi-nonparametric and the
semiparametric maximum likelihood estimators relative to a maximum
likelihood correctly specified estimator of a parametric probit are rather
small. On the other hand, a comparison of these estimators in non-Gaussian
designs suggests that semi-nonparametric and semiparametric maximum
likelihood estimators substantially dominate the parametric probit maximum
likelihood estimator.
View all articles by this author:
Giuseppe De Luca
View all articles with these keywords:
snp, snp2, snp2s, sml, sml2s, binary-choice models, semi-nonparametric approach, SNP estimation, semiparametric maximum likelihood, SML estimation, Monte Carlo simulation
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
