A Smoothed Maximum Score Estimator for the Binary Response Model
提出一种半参数估计量，用于处理可能存在未知形式异方差的二元响应模型，通过平滑Manski最大得分估计的目标函数得到，具有最优收敛速度和渐近正态性。
This paper describes a semiparametric estimator for binary response models in which there may be arbitrary heteroskedasticity of unknown form. The estimator is obtained by maximizing a smoothed version of the objective function of C. Manski's maximum score estimator. The smoothing procedure is similar to that used in kernel nonparametric density estimation. The resulting estimator's rate of convergence in probability is the fastest possible under the assumptions that are made. The centered, normalized estimator is asymptotically normally distributed. Methods are given for consistently estimating the parameters of the limiting distribution and for selecting the bandwidth required by the smoothing procedure. Copyright 1992 by The Econometric Society.