Arbeitspapier

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

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.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP40/11

Klassifikation
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Duration Analysis; Optimal Timing Strategies
Model Construction and Estimation
Forecasting Models; Simulation Methods
Thema
Quantile Regression
Feasible Inference
Extreme Value Theory

Ereignis
Geistige Schöpfung
(wer)
Chernozhukov, Victor
Fernández-Val, Iván
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2009

DOI
doi:10.1920/wp.cem.2011.4011
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Chernozhukov, Victor
  • Fernández-Val, Iván
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2009

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