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Composite Quantile Regression for the Single-Index Model

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Author Info

  • Yan Fan
  • Wolfgang Karl Härdle
  • Weining Wang
  • Lixing Zhu

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 specifi- cation. 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.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2013-010.

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Length: 43 pages
Date of creation: Feb 2013
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2013-010

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Related research

Keywords: Quantile Single-index Regression; Minimum Average Contrast Estimation; Co- VaR estimation; Composite quasi-maximum likelihood estimation; Lasso; Model selection;

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Cited by:
  1. Poeschel, Friedrich, 2012. "Assortative matching through signals," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62061, Verein für Socialpolitik / German Economic Association.

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