A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model
提出一种完全数据驱动的半参数样本选择模型估计量，通过最小化拟合模型的均方误差自动选择带宽，并证明其渐近正态性，蒙特卡洛模拟显示有限样本表现优异。
This paper proposes a simple fully data-driven version of Powell's (2001) two-step semiparametric estimator for the sample selection model. The main feature of the proposal is that the bandwidth used to estimate the infinite-dimensional nuisance parameter is chosen by minimizing the mean squared error of the fitted semiparametric model. We formally justify data-driven inference. We introduce the concept of asymptotic normality, uniform in the bandwidth, and show that the proposed estimator achieves this property for a wide range of bandwidths. The method of proof is different from that in Powell (2001 Powell , J. L. ( 2001 ). Semiparametric estimation of censored selection models . In: Hsiao , C. , Morimune , K. , Powell , J. , eds. Nonlinear Statistical Modeling . Cambridge : Cambridge University Press , pp. 165 – 96 .[Crossref] , [Google Scholar]) and permits straightforward extensions to other semiparametric or even fully nonparametric specifications of the selection equation. The results of a small Monte Carlo suggest that our estimator has excellent finite sample performance, comparing well with other competing estimators based on alternative choices of smoothing parameters.