Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/85198
Authors: 
Beran, Jan
Feng, Yuanhua
Gosh, Sucharita
Sibbertsen, Philipp
Year of Publication: 
2000
Series/Report no.: 
CoFE Discussion Paper 00/18
Abstract: 
Prediction in time series models with a trend requires reliable estima- tion of the trend function at the right end of the observed series. Local polynomial smoothing is a suitable tool because boundary corrections are included implicitly. However, outliers may lead to unreliable estimates, if least squares regression is used. In this paper, local polynomial smoothing based on M-estimators are asymptotically equivalent to the least square solution, under the (ideal) Gaussian model. Outliers turn out to have a major effect on nonrobust bandwidht selection, in particular due to the change of the dependence structure.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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