Arbeitspapier

Optimal smoothing for a computationally and statistically efficient single index estimator

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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.

Language: Englisch

Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2009,028

Classification: Wirtschaft
Mathematical and Quantitative Methods: General
Estimation: General
Semiparametric and Nonparametric Methods: General

Subject: ADE
Asymptotics
Bandwidth
MAVE method
Semi-parametric efficiency
Schätztheorie
Nichtparametrisches Verfahren
Theorie

Event: Geistige Schöpfung

(who): Xia, Yingcun
Härdle, Wolfgang Karl
Linton, Oliver

Event: Veröffentlichung

(who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

(where): Berlin

(when): 2009

Handle: http://hdl.handle.net/10419/25344

Last update: 10.03.2025, 11:44 AM CET

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Object type

Arbeitspapier

Associated

Xia, Yingcun
Härdle, Wolfgang Karl
Linton, Oliver
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk

Time of origin

2009

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