Fan, Yan Härdle, Wolfgang Karl Wang, Weining Zhu, Lixing
Year of Publication:
SFB 649 Discussion Paper 2013-010
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.
quantile single-index regression minimum average contrast estimation co-VaR estimation composite quasi-maximum likelihood estimation Lasso model selection