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Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity Author info | Abstract | Publisher info | Download info | Related research | Statistics 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)
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.
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics .
Volume (Year): 23 (2008)
Issue (Month): 6 ()
Pages: 699-728
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Handle: RePEc:jae:japmet:v:23:y:2008:i:6:p:699-728Contact details of provider: Web page: http://www.interscience.wiley.com/jpages/0883-7252/
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