Inference for extremal conditional quantile models, with an application to market and birthweight risks

Victor Chernozhukov and Ivan Fernandez-Val ()

No CWP40/11, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2011-12-27
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (2011)
Working Paper: Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (2009)
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