Publikation: On robust local polynomial estimation with Long-memory errors
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Prediction in time series models with a trend requires reliable estimation 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-estimation is considered for the case where the error process exhibits long-range dependence. In contrast to the iid case, all M-estimators are asymptotically equivalent to the least-square solution, under the (ideal) Gaussian model. The potential usefulness of the proposal for forecasting is illustrated by practical and simulated examples. A simulation study shows that outliers have a major effect on nonrobust bandwidth selection, in particular due to the change of the dependence structure.
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BERAN, Jan, Yuanhua FENG, Sucharita GHOSH, Philipp SIBBERTSEN, 2002. On robust local polynomial estimation with Long-memory errors. In: International Journal of Forecasting. 2002, 18(2), pp. 227-241. ISSN 0169-2070. eISSN 1872-8200. Available under: doi: 10.1016/S0169-2070(01)00155-8BibTex
@article{Beran2002robus-27569, year={2002}, doi={10.1016/S0169-2070(01)00155-8}, title={On robust local polynomial estimation with Long-memory errors}, number={2}, volume={18}, issn={0169-2070}, journal={International Journal of Forecasting}, pages={227--241}, author={Beran, Jan and Feng, Yuanhua and Ghosh, Sucharita and Sibbertsen, Philipp} }
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