Maksym, Obrizan (2010): A Bayesian Model of Sample Selection with a Discrete Outcome Variable.

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Abstract
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 nonignorable selection that can handle multiple selection and discretecontinuous 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 of the variance matrix to avoid the MetropolisHastings algorithm in the Gibbs sampler. Finally, using artificial data I show that the model is capable of retrieving the parameters used in the datagenerating process and also that the resulting Markov Chain passes all standard convergence tests.
Item Type:  MPRA Paper 

Original Title:  A Bayesian Model of Sample Selection with a Discrete Outcome Variable 
Language:  English 
Keywords:  Markov Chain Monte Carlo; sample selection; multivariate probit 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C35  Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  28577 
Depositing User:  Maksym Obrizan 
Date Deposited:  04. Feb 2011 07:56 
Last Modified:  01. Jan 2016 01:39 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/28577 