Conditional extremes and near-extremes
By: Victor Chernozhukov
Contributors: Massachusetts Institute of Technology. Dept. of Economics
Series: Working paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 01-21; Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 01-21.
Publisher: Massachusetts Institute of Technology, Dept. of Economics
Published: 2001
Place of publication: Cambridge, MA
Language: eng
Number of pages: 41
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.
Notes: Title from cover.

"July 2000."; "Version: May 10, 2001; first version: October, 1998 ... This paper is part of my Ph.D. dissertation at Stanford, completed in 2000."--Abstract.

Includes bibliographical references (p. 39-41).

Abstract in HTML and working paper for download in PDF available via World Wide Web at the Social Science Research Network.