Composite quantile regression for the single-index model

descriptionPublicationkeyboard_double_arrow_right Research , Preprint 01 Jan 2013 Germany English Publisher:Berlin: Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

Authors: Yan Fan; Wolfgang Karl Härdle; Weining Wang; Lixing Zhu;

handle: 10419/79568

Composite quantile regression for the single-index model

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Abstract

Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a projection based single index model specification. For very high dimensional regressors X one faces yet another dimensionality problem and needs to balance precision vs. dimension. Such a balance may be achieved by combining semiparametric ideas with variable selection techniques.

Country

Germany

Related Organizations

Humboldt-Universität zu Berlin
Germany
Humboldt-Universität zu Berlin
Renmin University of China
China (People's Republic of)
Humboldt-Universität zu Berlin

Keywords

model selection, minimum average contrast estimation, Composite quasi-maximum likelihood estimation, ddc:330, 330 Wirtschaft, quantile single-index regression, ddc:310, Co-VaR estimation, 310 Sammlungen allgemeiner Statistiken, composite quasi-maximum likelihood estimation, Model selection, Quantile Single-index Regression, co-VaR estimation, Quantile Single-index Regression, Minimum Average Contrast Estimation, Co- VaR estimation, Composite quasi-maximum likelihood estimation, Lasso, Model selection, Lasso, Minimum Average Contrast Estimation, jel: jel:C50, jel: jel:C00, jel: jel:C14, jel: jel:C58

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