Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25344
Authors: 
Xia, Yingcun
Härdle, Wolfgang Karl
Linton, Oliver
Year of Publication: 
2009
Series/Report no.: 
SFB 649 discussion paper 2009,028
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.
Subjects: 
ADE
Asymptotics
Bandwidth
MAVE method
Semi-parametric efficiency
JEL: 
C00
C13
C14
Document Type: 
Working Paper

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