Bayesian Inference in Spatial Sample Selection Models

Osman Doğan, Süleyman Taşpinar

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)90-121
Number of pages32
JournalOxford Bulletin of Economics and Statistics
Issue number1
Publication statusPublished - 1 Feb 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 The Department of Economics, University of Oxford and John Wiley & Sons Ltd


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