Maksym, Obrizan (2010): A Bayesian Model of Sample Selection with a Discrete Outcome Variable.
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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 non-ignorable selection that can handle multiple selection and discrete-continuous 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 Metropolis-Hastings algorithm in the Gibbs sampler. Finally, using artificial data I show that the model is capable of retrieving the parameters used in the data-generating 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|
|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
|Depositing User:||Maksym Obrizan|
|Date Deposited:||04. Feb 2011 07:56|
|Last Modified:||13. Feb 2013 17:47|
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