OLS and IV estimation of regression models including endogenous interaction terms
研究包含内生变量与外生协变量交互项的线性回归模型，提出在缺乏传统排除工具变量时利用结构方程的非线性函数形式生成工具变量，并证明当函数形式识别无效时OLS估计仍一致。
We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.