Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors

Publikation: Working paperForskning


  • rp19_05

    Forlagets udgivne version, 1,15 MB, PDF-dokument

  • Antoine A. Djogbenou, York University, Canada, Canada
  • James G. James G. MacKinnon, Queen's University, Canada
  • Morten Ørregaard Nielsen
We study inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap. We state conditions under which asymptotic and bootstrap tests and confidence intervals are asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. We also derive Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results and suggest that alternative variants of the wild cluster bootstrap may perform quite differently. The Edgeworth expansions explain the overrejection of asymptotic tests and shed light on the choice of auxiliary distribution and whether to use restricted or unrestricted estimates in the bootstrap data-generating process.
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider56
StatusUdgivet - 9 apr. 2019
SerietitelCREATES Research Papers


  • Clustered data, cluster-robust variance estimator, Edgeworth expansion, inference, wild cluster bootstrap

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