Arbeitspapier
Optimal smoothing for a computationally and statistically efficient single index estimator
In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators of the finite dimensional parameters can achieve root-n consistency. The requirement of under-smoothing may result as we show from inefficient estimation methods or technical difficulties. Based on local linear kernel smoother, we propose an estimation method to estimate the single-index model without under-smoothing. Under some conditions, our estimator of the single-index is asymptotically normal and most efficient in the semi-parametric sense. Moreover, we derive higher expansions for our estimator and use them to define an optimal bandwidth for the purposes of index estimation. As a result we obtain a practically more relevant method and we show its superior performance in a variety of applications.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2009,028
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
Estimation: General
Semiparametric and Nonparametric Methods: General
- Thema
-
ADE
Asymptotics
Bandwidth
MAVE method
Semi-parametric efficiency
Schätztheorie
Nichtparametrisches Verfahren
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Xia, Yingcun
Härdle, Wolfgang Karl
Linton, Oliver
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2009
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Xia, Yingcun
- Härdle, Wolfgang Karl
- Linton, Oliver
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2009