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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP40/11
- Classification
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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
- Subject
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Quantile Regression
Feasible Inference
Extreme Value Theory
- Event
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Geistige Schöpfung
- (who)
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Chernozhukov, Victor
Fernández-Val, Iván
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
-
2009
- DOI
-
doi:10.1920/wp.cem.2011.4011
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chernozhukov, Victor
- Fernández-Val, Iván
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2009
Other Objects (12)
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Conditional quantile processes based on series or many regressors
Robust inference in high-dimensional approximately sparse quantile regression models
Generic inference on quantile and quantile effect functions for discrete outcomes
Generic inference on quantile and quantile effect functions for discrete outcomes
Generic inference on quantile and quantile effect functions for discrete outcomes
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Quantile models with endogeneity
Quantile graphical models: Prediction and conditional independence with applications to systemic risk
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Conditional quantile processes based on series or many regressors
L1-Penalised quantile regression in high-dimensional sparse models
Extremal quantile regression: An overview
Conditional quantile processes based on series or many regressors
Robust inference in high-dimensional approximately sparse quantile regression models
Generic inference on quantile and quantile effect functions for discrete outcomes
Generic inference on quantile and quantile effect functions for discrete outcomes
Generic inference on quantile and quantile effect functions for discrete outcomes
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Quantile models with endogeneity
Quantile graphical models: Prediction and conditional independence with applications to systemic risk
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Conditional quantile processes based on series or many regressors