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dc.contributor.authorXia, Yingcunen_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorLinton, Oliveren_US
dc.date.accessioned2009-05-12en_US
dc.date.accessioned2009-07-23T15:15:31Z-
dc.date.available2009-07-23T15:15:31Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/25344-
dc.description.abstractIn 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.en_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2009,028en_US
dc.subject.jelC00en_US
dc.subject.jelC13en_US
dc.subject.jelC14en_US
dc.subject.ddc330en_US
dc.subject.keywordADEen_US
dc.subject.keywordAsymptoticsen_US
dc.subject.keywordBandwidthen_US
dc.subject.keywordMAVE methoden_US
dc.subject.keywordSemi-parametric efficiencyen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleOptimal smoothing for a computationally and statistically efficient single index estimatoren_US
dc.typeWorking Paperen_US
dc.identifier.ppn599994045en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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