Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64698
Authors: 
Chernozhukov, Victor
Fernández-Val, Iván
Year of Publication: 
2009
Series/Report no.: 
cemmap working paper CWP40/11
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' birthweights in the range between 250 and 1500 grams.
Subjects: 
Quantile Regression
Feasible Inference
Extreme Value Theory
JEL: 
C13
C14
C21
C41
C51
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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