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Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity
Author(s) -
Li Tong,
Zheng Xiaoyong
Publication year - 2008
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1017
Subject(s) - tobit model , econometrics , panel data , markov chain monte carlo , inference , bayesian probability , bayesian inference , economics , data set , computer science , statistics , mathematics , artificial intelligence
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.