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A Bayesian approach to dynamic Tobit models

Author

Listed:
  • WEI, Steven X.

    (Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium)

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.

Suggested Citation

  • WEI, Steven X., 1997. "A Bayesian approach to dynamic Tobit models," LIDAM Discussion Papers CORE 1997081, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:1997081
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp1997.html
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    Cited by:

    1. David Veredas & Juan Rodriguez-Poo & Antoni Espasa, 2001. "On the (Intradaily) Seasonality and Dynamics of a Financial Point Process : A Semiparametric Approach," Working Papers 2001-19, Center for Research in Economics and Statistics.

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