SEMIPARAMETRIC ESTIMATION OF NONSTATIONARY CENSORED PANEL DATA MODELS WITH TIME VARYING FACTOR LOADS
提出一种半参数面板数据删失回归模型的估计方法，允许误差项存在一般形式的非平稳性，如时变异方差和个体效应的时变因子载荷，可用于研究不可观测技能回报随时间的变化或代际收入相关性。
We propose an estimation procedure for a semiparametric panel data censored regression model in which the error terms may be subject to general forms of nonstationarity. Specifically, we allow for heteroskedasticity over time and a time varying factor load on the individual specific effect. Empirically, estimation of this model would be of interest to explore how returns to unobserved skills change over time—see, e.g., Chay (1995, manuscript, Princeton University) and Chay and Honoré (1998, Journal of Human Resources 33, 4–38). We adopt a two-stage procedure based on nonparametric median regression, and the proposed estimator is shown to be $\sqrt{n}$ -consistent and asymptotically normal. The estimation procedure is also useful in the group effect setting, where estimation of the factor load would be empirically relevant in the study of the intergenerational correlation in income, explored in Solon (1992, American Economic Review 82, 393–408; 1999, Handbook of Labor Economics , vol. 3, 1761–1800) and Zimmerman (1992, American Economic Review 82, 409–429).