Identification of Instrumental Variable Correlated Random Coefficients Models
研究了连续内生回归变量下工具变量相关随机系数模型中平均偏效应的识别与估计，提出通过一组普通线性回归的系数平均来识别该效应，并给出了可用Stata实现的简单估计量。
We study identification and estimation of the average partial effect in an instrumental variable correlated random coefficients model with continuously distributed endogenous regressors. This model allows treatment effects to be correlated with the level of treatment. The main result shows that the average partial effect is identified by averaging coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. These control functions can be constructed from binary or discrete instruments, which may affect the endogenous variables heterogeneously. Our results suggest a simple estimator that can be implemented with a companion Stata module.