Efficient Estimation of an Additive Quantile Regression Model

Yebin Cheng, Jan G. Gooijer and Dawit Zerom (dzerom@fullerton.edu)

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Additive models; Asymptotic properties; Dependent data; Internalized kernel smoothing; Local polynomial; Oracle efficiency (search for similar items in EconPapers)
JEL-codes: C01 C14 (search for similar items in EconPapers)
Date: 2009-03-14
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Efficient Estimation of an Additive Quantile Regression Model (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:14388

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