Tobit Bayesian Model Averaging and the Determinants of Foreign Direct Investment

Alexander Jordan and Alex Lenkoski

Papers from arXiv.org

Abstract: We develop a fully Bayesian, computationally efficient framework for incorporating model uncertainty into Type II Tobit models and apply this to the investigation of the determinants of Foreign Direct Investment (FDI). While direct evaluation of modelprobabilities is intractable in this setting, we show that by using conditional Bayes Factors, which nest model moves inside a Gibbs sampler, we are able to incorporate model uncertainty in a straight-forward fashion. We conclude with a study of global FDI flows between 1988-2000.

Date: 2012-05
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1205.2501

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