Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas

Phillip Li and Mohammad Arshad Rahman ()

A chapter in Missing Data Methods: Cross-sectional Methods and Applications, 2011, pp 269-288 from Emerald Group Publishing Limited

Abstract: We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.

Keywords: Bayesian estimation; Markov chain; Monte Carlo; Gaussian copulas; sample selection (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2011)000027a013

DOI: 10.1108/S0731-9053(2011)000027A013

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