Munich Personal RePEc Archive

Efficient Estimation of an Additive Quantile Regression Model

Cheng, Yebin and De Gooijer, Jan and Zerom, Dawit (2009): Efficient Estimation of an Additive Quantile Regression Model.

[img]
Preview
PDF
MPRA_paper_14388.pdf

Download (370kB) | Preview

Abstract

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). With the aim to reduce variance of the first estimator, a second estimator is defined via sequential fitting of univariate local polynomial quantile smoothing for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The second estimator achieves oracle efficiency in the sense that each estimated additive component has the same variance as in the case when all other additive components were known. Asymptotic properties are derived for both estimators under dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative empirical application of additive quantile models to ambulance travel times.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.