Fast and reliable jackknife and bootstrap methods for cluster‐robust inference

We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, bootstrap data-generating processes, or both. Extensive simulation experiments suggest that can much more reliable inferences than existing ones in cases where latter are not trustworthy, such as when number clusters is small and/or sizes vary substantially. Three empirical examples illustrate methods.