Statistics – Methodology
Scientific paper
2009-12-26
Statistics
Methodology
41 pages, 9 figures
Scientific paper
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.
Chernozhukov Victor
Fernandez-Val Ivan
No associations
LandOfFree
Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-122330