Estimation of Ordered Response Models with Sample Selection

28 Pages Posted: 4 Jun 2010

See all articles by Giuseppe De Luca

Giuseppe De Luca

University of Palermo - d/SEAS

Valeria Perotti

affiliation not provided to SSRN

Date Written: June 3, 2010

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 (ML) 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 (SNP) approach of Gallant and Nychka (1987) 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 SNP estimation of discrete response models. Compared to the other SNP estimators, our routine is relatively faster because it is programmed in MATA. In addition, we provide new post-estimation routines to compute linear predictions, predicted probabilities and marginal effects. These improvements are also extended to the set of SNP Stata commands originally written by Stewart (2004) and De Luca (2008). 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.

Keywords: Ordered response models, sample selection, parametric ML estimation, semi-nonparametric

Suggested Citation

De Luca, Giuseppe and Perotti, Valeria, Estimation of Ordered Response Models with Sample Selection (June 3, 2010). CEIS Working Paper No. 168, Available at SSRN: https://ssrn.com/abstract=1619783 or http://dx.doi.org/10.2139/ssrn.1619783

Giuseppe De Luca (Contact Author)

University of Palermo - d/SEAS ( email )

Viale delle Scienze, edificio 13
Palermo, 90124
Italy

Valeria Perotti

affiliation not provided to SSRN ( email )

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
301
Abstract Views
1,437
rank
127,403
PlumX Metrics