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 … methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests …

prove the asymptotic validity of a wild bootstrap implementation. The proposed tests work for a null hypothesis of either no … determine the appropriate level of clustering. Simulations suggest that the bootstrap tests perform very well under the null …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and …

under are highly nonstandard due to the inherent irregular natures of the problem, and then construct bootstrap critical …

disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can … dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the … same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and …