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https://hdl.handle.net/10419/25344
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Xia, Yingcun | en |
dc.contributor.author | Härdle, Wolfgang Karl | en |
dc.contributor.author | Linton, Oliver | en |
dc.date.accessioned | 2009-05-12 | - |
dc.date.accessioned | 2009-07-23T15:15:31Z | - |
dc.date.available | 2009-07-23T15:15:31Z | - |
dc.date.issued | 2009 | - |
dc.identifier.uri | http://hdl.handle.net/10419/25344 | - |
dc.description.abstract | 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. | en |
dc.language.iso | eng | en |
dc.publisher | |aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlin | en |
dc.relation.ispartofseries | |aSFB 649 Discussion Paper |x2009,028 | en |
dc.subject.jel | C00 | en |
dc.subject.jel | C13 | en |
dc.subject.jel | C14 | en |
dc.subject.ddc | 330 | en |
dc.subject.keyword | ADE | en |
dc.subject.keyword | Asymptotics | en |
dc.subject.keyword | Bandwidth | en |
dc.subject.keyword | MAVE method | en |
dc.subject.keyword | Semi-parametric efficiency | en |
dc.subject.stw | Schätztheorie | en |
dc.subject.stw | Nichtparametrisches Verfahren | en |
dc.subject.stw | Theorie | en |
dc.title | Optimal smoothing for a computationally and statistically efficient single index estimator | - |
dc.type | |aWorking Paper | en |
dc.identifier.ppn | 599994045 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
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