Reworking wild bootstrap‐based inference for clustered errors

Abstract Cluster‐robust inference is increasingly common in empirical research. With few clusters, inference is often conducted using the wild cluster bootstrap. With conventional bootstrap weights the set of valid ‐values can create ambiguities in inference. I consider several modifications to the bootstrap procedure to resolve these ambiguities. Monte Carlo simulations provide evidence that both a new 6‐point bootstrap weight distribution and a kernel density estimation approach improve the reliability of inference. A brief empirical example highlights the implications of these findings.