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Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >
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http://hdl.handle.net/10419/25344
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| Title: | | Optimal smoothing for a computationally and statistically efficient single index estimator  |
| Authors: | | Xia, Yingcun Härdle, Wolfgang Karl Linton, Oliver |
| Issue Date: | | 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 |
| Appears in Collections: | | SFB 649 Discussion Papers, HU Berlin
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