Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks

42 Pages Posted: 16 Aug 2011

See all articles by Victor Chernozhukov

Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics

Iván Fernández‐Val

Boston University - Department of Economics

Date Written: July 12, 2011

Abstract

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants’ birth weights in the range between 250 and 1500 grams.

Keywords: Quantile Regression, Feasible Inference, Extreme Value Theory

JEL Classification: C13, C14, C21, C41, C51, C53

Suggested Citation

Chernozhukov, Victor and Fernandez-Val, Ivan, Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (July 12, 2011). MIT Department of Economics Working Paper No. 11-18, Available at SSRN: https://ssrn.com/abstract=1910056 or http://dx.doi.org/10.2139/ssrn.1910056

Victor Chernozhukov (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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HOME PAGE: http://www.mit.edu/~vchern/

Ivan Fernandez-Val

Boston University - Department of Economics ( email )

270 Bay State Road
Boston, MA 02215
United States

HOME PAGE: http://people.mit.edu/ivanf

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