Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors
提出两种新方法估计含不可分误差和内生回归变量的模型：第一种估计局部平均响应，第二种估计不可分函数及可观测与不可观测变量的联合分布，适用于截面和面板数据。
We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group. Copyright The Econometric Society 2005.