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Bayesian inference in a sample selection model

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  • van Hasselt, Martijn
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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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 165 (2011)
    Issue (Month): 2 ()
    Pages: 221-232

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    Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:221-232

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    Web page: http://www.elsevier.com/locate/jeconom

    Related research

    Keywords: Sample selection; Gibbs sampling; Mixture distributions; Dirichlet process;

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    Cited by:
    1. Ding, Peng, 2014. "Bayesian robust inference of sample selection using selection-t models," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 451-464.
    2. Rong Zhang & Brett A. Inder & Xibin Zhang, 2012. "Parameter estimation for a discrete-response model with double rules of sample selection: A Bayesian approach," Monash Econometrics and Business Statistics Working Papers 5/12, Monash University, Department of Econometrics and Business Statistics.
    3. Rong Zhang & Brett A. Inder & Xibin Zhang, 2013. "Bayesian estimation of a discrete response model with double rules of sample selection," Monash Econometrics and Business Statistics Working Papers 24/13, Monash University, Department of Econometrics and Business Statistics.
    4. Anastasios Panagiotelis & Michael S. Smith & Peter J Danaher, 2013. "From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior," Monash Econometrics and Business Statistics Working Papers 5/13, Monash University, Department of Econometrics and Business Statistics.

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