The MCMC and SML estimation of a self-selection model with two outcomes

Abstract

A self-selection model with discrete and continuous outcomes and a treatment variable is considered. The treatment variable is endogenous to the two outcome variables. Two estimation procedures are proposed and compared. The first estimation approach is Bayesian and uses the Markov Chain Monte Carlo (MCMC) methods. It constructs stationary Markov chains that converge to the posterior distribution of the parameters of the model. The second one is a full information maximum likelihood approach, using the simulated maximum likelihood (SML). estimator. Both methods are tested on a numerical example.