Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our mo...

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Bibliographic Details
Published in:Oxford bulletin of economics and statistics 2018-02, Vol.80 (1), p.90-121
Main Authors: Doan, Osman, Taspinar, Suleyman
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Markov analysis
Monte Carlo simulation
Selection bias
Simulation
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Summary:In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.
ISSN:0305-9049
1468-0084
DOI:10.1111/obes.12187