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Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity

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  • Tong Li

    (Department of Economics, Vanderbilt University, Nashville, TN, USA)

  • Xiaoyong Zheng

    (Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC, USA)

Abstract

This paper develops semiparametric Bayesian methods for inference of dynamic Tobit panel data models. Our approach requires that the conditional mean dependence of the unobserved heterogeneity on the initial conditions and the strictly exogenous variables be specified. Important quantities of economic interest such as the average partial effect and average transition probabilities can be readily obtained as a by-product of the Markov chain Monte Carlo run. We apply our method to study female labor supply using a panel data set from the National Longitudinal Survey of Youth 1979. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Tong Li & Xiaoyong Zheng, 2008. "Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 699-728.
  • Handle: RePEc:jae:japmet:v:23:y:2008:i:6:p:699-728
    DOI: 10.1002/jae.1017
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    File URL: http://qed.econ.queensu.ca:80/jae/2008-v23.6/
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    References listed on IDEAS

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    Cited by:

    1. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.

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