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Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas

In: Missing Data Methods: Cross-sectional Methods and Applications

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  • Phillip Li
  • Mohammad Arshad Rahman

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.

Suggested Citation

  • Phillip Li & Mohammad Arshad Rahman, 2011. "Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 269-288, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(2011)000027a013
    DOI: 10.1108/S0731-9053(2011)000027A013
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