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

Authors:
Murat K. Munkin
Affiliations:
Department of Economics, University of Tennessee, 503 Stokely Management Center, Knoxville, TN
Venue:
Computational Statistics & Data Analysis - Special issue: Computational econometrics
Year:
2003

Citing 2
Cited 2

Stochastic simulation

Stochastic simulation
Multivariate statistical simulation

Multivariate statistical simulation

Preface: Second Special issue on Computational Econometrics

Computational Statistics & Data Analysis
A finite mixture model for multivariate counts under endogenous selectivity

Statistics and Computing

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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 mode. 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.