Logo
Munich Personal RePEc Archive

A Bayesian Model of Sample Selection with a Discrete Outcome Variable

Maksym, Obrizan (2010): A Bayesian Model of Sample Selection with a Discrete Outcome Variable.

[thumbnail of MPRA_paper_28577.pdf]
Preview
PDF
MPRA_paper_28577.pdf

Download (584kB) | Preview

Abstract

Relatively few published studies apply Heckman’s (1979) sample selection model to the case of a discrete endogenous variable and those are limited to a single outcome equation. However, there are potentially many applications for this model in health, labor and financial economics. To fill in this theoretical gap, I extend the Bayesian multivariate probit setup of Chib and Greenberg (1998) into a model of non-ignorable selection that can handle multiple selection and discrete-continuous outcome equations. The first extension of the multivariate probit model in Chib and Greenberg (1998) allows some of the outcomes to be missing. In addition, I use Cholesky factorization of the variance matrix to avoid the Metropolis-Hastings algorithm in the Gibbs sampler. Finally, using artificial data I show that the model is capable of retrieving the parameters used in the data-generating process and also that the resulting Markov Chain passes all standard convergence tests.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.