Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks
Massachusetts Institute of Technology (MIT) - Department of Economics; New Economic School
Boston University - Department of Economics
July 12, 2011
MIT Department of Economics Working Paper No. 11-18
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.
Number of Pages in PDF File: 42
Keywords: Quantile Regression, Feasible Inference, Extreme Value Theory
JEL Classification: C13, C14, C21, C41, C51, C53working papers series
Date posted: August 16, 2011
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