Wild Bootstrap Tests for IV Regression
提出一种用于工具变量线性回归的野自助法，能处理未知形式的异方差，应用于t检验和Anderson-Rubin检验，模拟显示优于传统方法，并给出构建置信区间的方法。
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.