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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP07/04
- Classification
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Wirtschaft
- Subject
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Nichtparametrisches Verfahren
Regression
Schätztheorie
- Event
-
Geistige Schöpfung
- (who)
-
Horowitz, Joel L.
Lee, Sokbae
- Event
-
Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
-
2004
- DOI
-
doi:10.1920/wp.cem.2004.0704
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Horowitz, Joel L.
- Lee, Sokbae
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2004
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