A REFINED BOOTSTRAP FOR HEAVY TAILED DISTRIBUTIONS
Russell Davidson and
Adriana Cornea-Madeira ()
Departmental Working Papers from McGill University, Department of Economics
It is known that Efron's nonparametric bootstrap of the mean of random variables with common distribution in the domain of attraction of the stable laws is not consistent, in the sense that the limiting distribution of the bootstrap mean is not the same as the limiting distribution of the mean from the real sample. Moreover, the limiting distribution of the bootstrap mean is random and unknown. The remedy for this problem, at least asymptomatically, is either the m out of n or the subsampling bootstrap. However, we show that both these bootstraps can be quite unreliable if the sample is not very large. A refined bootstrap is derived by considering the distribution of the bootstrap P value instead of that of the bootstrap statistic. The quality of inference based on the refined bootstrap is examined in a simulation study, and is found to be satisfactory with heavy-tailed distributions unless the tail index is close to 1 and the distribution is heavily skewed.
JEL-codes: C12 C15 (search for similar items in EconPapers)
Pages: 22 pages
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden (http://www.mcgill.ca/files/economics/arefinedbootstrapr.pdf [301 Moved Permanently]--> https://www.mcgill.ca/files/economics/arefinedbootstrapr.pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:mcl:mclwop:2008-03
Access Statistics for this paper
More papers in Departmental Working Papers from McGill University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Shama Rangwala ( this e-mail address is bad, please contact ).