Conditional extremes and near-extremes
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Author
Contributions
- Massachusetts Institute of Technology. Dept. of Economics - Contributor
Publication
2001 - Massachusetts Institute of Technology, Dept. of Economics, Cambridge, MA, Massachusetts
Language
English
Word Count
10,250 words, Guess
Page Count
41 pages
Identifiers
- Internet Archiveconditionalextre00cher
- OCLC Control Number49829433
- Open LibraryOL24639681M
Description
This paper develops a theory of high and low (extremal) quantile regression: the linear models, estimation, and inference. In particular, the models coherently combine the convenient, flexible linearity with the extreme-value-theoretic restrictions on tails and the general heteroscedasticity forms. Within these models, the limit laws for extremal quantile regression statistics are obtained under the rank conditions (experiments) constructed to reflect the extremal or rare nature of tail events. An inference framework is discussed. The results apply to cross-section (and possibly dependent) data. The applications, ranging from the analysis of babies' very low birth weights, (S,s) models, tail analysis in heteroscedastic regression models, outlier-robust inference in auction models, and decision-making under extreme uncertainty, provide the motivation and applications of this theory. Keywords: Quantile regression, extreme value theory, tail analysis, (S,s) models, auctions, price search, Extreme Risk. JEL Classifications: C13, C14, C21, C41, C51, C53, D21, D44, D81.
Subjects
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