Arbeitspapier

Efficient Estimation of an Additive Quantile Regression

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). By making use of an internally normalized kernel smoother, the proposed estimator reduces the computational requirement of the latter by the order of the sample size. The second estimator involves sequential fitting by univariate local polynomial quantile regressions for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The purpose of the extra local averaging is to reduce the variance of the first estimator. We show that 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 using administrative data for the city of Calgary.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. 09-104/4

Klassifikation
Wirtschaft
Econometrics
Semiparametric and Nonparametric Methods: General
Thema
Additive models
Asymptotic properties
Dependent data
Internalized kernel smoother
Local polynomial
Oracle efficiency
Regression
Nichtparametrisches Verfahren
Theorie

Ereignis
Geistige Schöpfung
(wer)
Cheng, Yebin
Gooijer, Jan G. De
Zerom, Dawit
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2009

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Cheng, Yebin
  • Gooijer, Jan G. De
  • Zerom, Dawit
  • Tinbergen Institute

Entstanden

  • 2009

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