Efficient Estimation of an Additive Quantile Regression Model
AbstractIn 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 14388.
Date of creation: 14 Mar 2009
Date of revision:
Additive models; Asymptotic properties; Dependent data; Internalized kernel smoothing; Local polynomial; Oracle efficiency;
Other versions of this item:
- Yebin Cheng & Jan G. De Gooijer & Dawit Zerom, 2011. "Efficient Estimation of an Additive Quantile Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 38(1), pages 46-62, 03.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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