Bayesian Inference in a Sample Selection Model

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Martijn Van Hasselt, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment.

Additional Information

Journal of Econometrics, 165(2), 221–232
Language: English
Date: 2011
Sample selection, Gibbs sampling, Mixture distributions, Dirichlet process

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