Fast and reliable jackknife and bootstrap methods for cluster‐robust inference
James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Abstract
Summary We provide computationally attractive methods to obtain jackknife‐based cluster‐robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife‐based bootstrap data‐generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.
Topics & Concepts
Jackknife resamplingEstimatorComputer scienceCluster (spacecraft)Variance (accounting)InferenceRegressionStatisticsEconometricsData miningMathematicsArtificial intelligenceBusinessAccountingProgramming languageStatistical Methods and InferenceStatistical Methods and Bayesian InferenceAdvanced Statistical Methods and Models