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

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

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

Entstanden

  • 2009

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