Estimation of ordered response models with sample selection

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.


Issue Date:
2011
Publication Type:
Journal Article
DOI and Other Identifiers:
st0226 (Other)
PURL Identifier:
http://purl.umn.edu/180078
Published in:
Stata Journal, Volume 11, Number 2
Page range:
213-239
Total Pages:
27

Record appears in:



 Record created 2017-04-01, last modified 2017-08-13

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)