Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity
提出半参数贝叶斯方法，用于推断动态Tobit面板数据模型，可计算平均部分效应和平均转移概率，并应用于1979年全国青年纵向调查的女性劳动供给数据。
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.