Consistency without Inference: Instrumental Variables in Practical Application
通过蒙特卡洛模拟和重抽样方法，研究了30篇AEA期刊论文中1309个工具变量回归，发现非独立同分布误差和高杠杆回归会降低IV检验的有效性，且弱工具变量预检验信息不足。
I use Monte Carlo simulations, the jackknife and multiple forms of the bootstrap to study a comprehensive sample of 1309 instrumental variables regressions in 30 papers published in the journals of the American Economic Association. Monte Carlo simulations based upon published regressions show that non-iid error processes in highly leveraged regressions, both prominent features of published work, adversely affect the size and power of IV tests, while increasing the bias and mean squared error of IV relative to OLS. Weak instrument pre-tests based upon F-statistics are found to be largely uninformative of both size and bias. In published papers IV has little power as, despite producing substantively different estimates, it rarely rejects the OLS point estimate or the null that OLS is unbiased, while the statistical significance of excluded instruments is exaggerated.