A review of more than one hundred Pareto-tail index estimators
Igor Fedotenkov
MPRA Paper from University Library of Munich, Germany
Abstract:
This paper reviews more than one hundred Pareto (and equivalent) tail index estimators. It focuses on univariate estimators for nontruncated data. We discuss basic ideas of these estimators and provide their analytical expressions. As samples from heavy-tailed distributions are analysed by researchers from various fields of science, the paper provides nontechnical explanations of the methods, which could be understood by researchers with intermediate skills in statistics. We also discuss strengths and weaknesses of the estimators, if they are known. The paper can be viewed as a catalog or a reference book on Pareto-tail index estimators.
Keywords: Heavy tails; Pareto distribution; tail index; review; extreme value index (search for similar items in EconPapers)
JEL-codes: C13 C14 C18 C58 (search for similar items in EconPapers)
Date: 2018-11-15
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/90072/1/MPRA_paper_90072.pdf original version (application/pdf)
Related works:
Journal Article: A Review of More than One Hundred Pareto-Tail Index Estimators (2020)
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:pra:mprapa:90072
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter (winter@lmu.de).