JSM 2005 - Toronto

Abstract #304272

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 103
Type: Contributed
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #304272
Title: Inference for Extremal Quantile Regression Models with an Application to Birth Weights
Author(s): Victor Chernozhukov*+
Companies: Massachusetts Institute of Technology
Address: 50 Memorial Drive E52 262F, Cambridge, MA, 02142, United States
Keywords: extreme value theory ; quantile regression ; resampling ; feasible inference ; birthweights
Abstract:

Quantile regression is a basic tool for estimation of conditional quantiles of a response variable given a vector of regressors. It can be used to measure the effect of regressors in the center of a distribution and in the upper and lower tails. Quantile regression applied to the tails, or simply extremal quantile regression, is of interest in numerous applications. For example, it can be employed to measure conditional value-at-risk, production efficiency, adjustment bands in the (S,s) models, and cost functions of most efficient bidders in auctions. In order to facilitate applications, this paper provides feasible inference tools that rely upon extreme value approximations of the distribution of self-scaled extremal quantile regression statistics. The methods are simple to apply in practice. The value of these methods is explored in the analysis of extremely low percentiles of live infant birth weights (in the ranges between 250 and 1,500 grams) and in the study of factors of extreme fluctuations of a stock returns.

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Revised March 2005