Estimating Derivatives in Nonseparable Models With Limited Dependent Variables
提出一种估计可观测变量X对有限因变量Y影响的方法，适用于Y是X和不可观测变量的不可分函数且X与不可观测变量独立的情况，通过修正删截样本的选择偏差来估计导数。
We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables, and X is independent of the unobservables. We treat models in which Y is censored from above, below, or both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of x on the censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both cross section and panel data contexts.