A Bayesian approach to dynamic Tobit models

Steven X. Wei
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Steven X. Wei: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium

No 1997081, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: This paper develops a posterior simulation method for a dynamic Tobit model. The major ob- stacle rooted in such a problem lies in high dimensional integrals, induced by dependence among censored observations, in the likelihood function. The primary contribution of this research is to develop a practical and efficient sampling scheme for the conditional posterior distributions of the censored (i.e.unobserved) data, so that the Gibbs sampler with data augmentation al- gorithm is successfully applied. The substantial differences between this approach and some existing methods are highlighted. The proposed simulation method is investigated by means of a Monte Carlo study and applied to a regression model of Japanese exports of passenger cars to the U.S. subject to a non-tariff trade barrier.

Keywords: Bayesian inference; dynamic Tobit model; Gibbs sampler with data augmentation; Monte Carlo simulation; truncated normal (search for similar items in EconPapers)
Date: 1997-10-01
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:1997081

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