Wild bootstrap of the sample mean in the infinite variance case

Giuseppe Cavaliere, Iliyan Georgiev, A. M Robert Taylor

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


It is well known that the standard independent, identically distributed (iid) bootstrap of the mean is inconsistent in a location model with infinite variance (alpha-stable) innovations. This occurs because the bootstrap distribution of a normalised sum of infinite variance random variables tends to a random distribution. Consistent bootstrap algorithms based on subsampling methods have been proposed but have the drawback that they deliver much wider confidence sets than those generated by the iid bootstrap owing to the fact that they eliminate the dependence of the bootstrap distribution on the sample extremes. In this paper we propose sufficient conditions that allow a simple modification of the bootstrap (Wu, 1986) to be consistent (in a conditional sense) yet to also reproduce the narrower confidence sets of the iid bootstrap. Numerical results demonstrate that our proposed bootstrap method works very well in practice delivering coverage rates very close to the nominal level and significantly narrower confidence sets than other consistent methods.
Original languageEnglish
Pages (from-to)204-219
JournalEconometric Reviews
Issue number2
Publication statusPublished - 1 Jan 2013


  • Bootstrap
  • Random probability measures
  • Stabel distributions


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