Dummy Endogenous Variables in Weakly Separable Models
研究弱可分模型中虚拟内生变量的平均效应的非参数识别与估计，不依赖参数函数形式或分布假设，适用于离散选择等有限因变量模型。
In this paper, we consider the nonparametric identification and estimation of the average effect of a dummy endogenous regressor in models where the regressors are weakly but not additively separable from the error term. The model is not required to be strictly increasing in the error term, and the class of models considered includes limited dependent variable models such as discrete choice models. Conditions are established conditions under which it is possible to identify the average effect of the dummy endogenous regressor in a weakly separable model without relying on parametric functional form or distributional assumptions and without the use of large support conditions. Copyright The Econometric Society 2007.