Estimation of ordered response models with sample selection
Giuseppe De Luca
Istituto per lo Sviluppo della Formazione Professionale dei Lavoratori
Rome, Italy
[email protected]
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Valeria Perotti
The World Bank
Washington, DC
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Abstract. We introduce two new Stata commands for the estimation of an ordered
response model with sample selection. The opsel command uses a standard
maximum-likelihood approach to fit a parametric specification of the model where
errors are assumed to follow a bivariate Gaussian distribution. The snpopsel
command uses the semi-nonparametric approach of Gallant and Nychka (1987,
Econometrica 55: 363–390) to fit a semiparametric specification of the model
where the bivariate density function of the errors is approximated by a Hermite
polynomial expansion. The snpopsel command extends the set of Stata routines
for semi-nonparametric estimation of discrete response models. Compared to the
other semi-nonparametric estimators, our routine is relatively faster because it
is programmed in Mata. In addition, we provide new postestimation routines
to compute linear predictions, predicted probabilities, and marginal effects. These
improvements are also extended to the set of semi-nonparametric Stata commands
originally written by Stewart (2004, Stata Journal 4: 27–39) and De Luca (2008,
Stata Journal 8: 190–220). An illustration of the new opsel and snpopsel commands
is provided through an empirical application on self-reported health with
selectivity due to sample attrition.
View all articles by these authors:
Giuseppe De Luca, Valeria Perotti
View all articles with these keywords:
opsel, opsel postestimation, sneop, sneop postestimation, snp2, snp2 postestimation, snp2s, snp2s postestimation, snpopsel, snpopsel postestimation, snp, snp postestimation, ordered response models, sample selection, parametric maximum-likelihood estimation, semi-nonparametric estimation
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