A Bayesian Model of Sample Selection with a Discrete Outcome Variable

摘要

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 ofthe 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 Chainpasses all standard convergence tests.