Composite quantile regression for the single-index model - Yan Fan; Wolfgang Karl Härdle; Weining Wang; Lixing Zhu (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2013)
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.
Keywords: 330; quantile single-index regression; minimum average contrast estimation; co-VaR estimation; composite quasi-maximum likelihood estimation; Lasso; model selection