Extremal Quantile Regression: An Overview

Victor Chernozhukov, Iv\'an Fern\'andez-Val and Tetsuya Kaji

Papers from arXiv.org

Abstract: Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with an increasing number of economic and financial applications such as value-at-risk, production frontiers, determinants of low infant birth weights, and auction models. This chapter provides an overview of recent developments in the theory and empirics of extremal quantile regression. The advances in the theory have relied on the use of extreme value approximations to the law of the Koenker and Bassett (1978) quantile regression estimator. Extreme value laws not only have been shown to provide more accurate approximations than Gaussian laws at the tails, but also have served as the basis to develop bias corrected estimators and inference methods using simulation and suitable variations of bootstrap and subsampling. The applicability of these methods is illustrated with two empirical examples on conditional value-at-risk and financial contagion.

Date: 2016-12, Revised 2017-02
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (10)

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http://arxiv.org/pdf/1612.06850 Latest version (application/pdf)

Related works:
Working Paper: Extremal quantile regression: an overview (2017)
Working Paper: Extremal quantile regression: an overview (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1612.06850

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