An extreme quantile estimator for the log-generalized Weibull-tail model

Clément Albert, Anne Dutfoy, Laurent Gardes and Stéphane Girard

Econometrics and Statistics, 2020, vol. 13, issue C, 137-174

Abstract: A new estimator for extreme quantiles is proposed under the log-generalized Weibull-tail model. This model relies on a new regular variation condition which, in some situations, permits to extrapolate further into the tails than the classical assumption in extreme-value theory. The asymptotic normality of the estimator is established and its finite sample properties are illustrated both on simulated and real datasets.

Keywords: Extreme quantile; Extreme-value theory; Extended regular variation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2452306219300097
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:13:y:2020:i:c:p:137-174

DOI: 10.1016/j.ecosta.2019.01.004

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().