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 (Figure presented.) -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.
CITATION STYLE
Webb, M. D. (2023). Reworking wild bootstrap-based inference for clustered errors. Canadian Journal of Economics, 56(3), 839–858. https://doi.org/10.1111/caje.12661
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