Arbeitspapier
Nonparametric estimation of an additive quantile regression model
This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the additive components are r-times continuously differentiable for some r ≥ 2. This result holds regardless of the dimension of the covariates and, therefore, the new estimator has no curse of dimensionality. In addition, the estimator has an oracle property and is easily extended to a generalized additive quantile regression model with a link function. The numerical performance and usefulness of the estimator are illustrated by Monte Carlo experiments and an empirical example.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP07/04
Klassifikation
Wirtschaft
Thema
Nichtparametrisches Verfahren
Regression
Schätztheorie
Regression
Schätztheorie
Ereignis
Geistige Schöpfung
(wer)
Horowitz, Joel L.
Lee, Sokbae
Lee, Sokbae
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2004
- DOI
-
doi:10.1920/wp.cem.2004.0704
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
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.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Horowitz, Joel L.
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2004
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