A bayesian approach to dynamic tobit models

This paper develops a posterior simulation method for a dynamic Tobit model. The major obstacle 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 study is to develop a practical...

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Bibliographic Details
Published inEconometric reviews Vol. 18; no. 4; pp. 417 - 439
Main Author Wei, Steven X.
Format Journal Article
LanguageEnglish
Published Marcel Dekker, Inc 01.01.1999
Taylor and Francis Journals
SeriesEconometric Reviews
Subjects
Algorithms
Bayesian inference
Bayesian method
Dynamic Tobit model
Econometrics
Monte Carlo simulation
Regression analysis
Sampling
The Gibbs sampler with the data augmentation
truncated normal distribution
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Summary:This paper develops a posterior simulation method for a dynamic Tobit model. The major obstacle 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 study 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 the data augmentation algorithm 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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0747-4938
1532-4168
DOI:10.1080/07474939908800353