Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
2009-05-13Buch DOI: 10.18452/4193
Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator
Xia, Yingcun
Härdle, Wolfgang Karl cc
Linton, Oliver
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.
Files in this item
Thumbnail
28.pdf — Adobe PDF — 452.5 Kb
MD5: 8497d688c28e50124cf52e3ea8f6264d
Cite
BibTeX
EndNote
RIS
InCopyright
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/4193
Permanent URL
https://doi.org/10.18452/4193
HTML
<a href="https://doi.org/10.18452/4193">https://doi.org/10.18452/4193</a>