The wild bootstrap with a “small” number of “large” clusters

Ivan A. Canay, Andres Santos, Azeem M. Shaikh

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


This paper studies the wild bootstrap–based test proposed in Cameron, Gelbach, and Miller (2008). Existing analyses of its properties require that number of clusters is “large.” In an asymptotic framework in which the number of clusters is “small,” we provide conditions under which an unstudentized version of the test is valid. These conditions include homogeneity-like restrictions on the distribution of covariates. We further establish that a studentized version of the test may only overreject the null hypothesis by a “small” amount that decreases exponentially with the number of clusters. We obtain a qualitatively similar result for “score” bootstrap-based tests, which permit testing in nonlinear models.

Original languageEnglish (US)
Pages (from-to)346-363
Number of pages18
JournalReview of Economics and Statistics
Issue number2
StatePublished - May 14 2021

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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