On robust local polynominal estimation with long-memory errors

Jan Beran, Yuanhua Feng, Sucharita Ghosh and Philipp Sibbertsen

No 2000,35, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: 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 constrast to the iid case, all 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 bandwidth selection, in particular due to the change of the dependence structure.

Date: 2000
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Related works:
Journal Article: On robust local polynomial estimation with long-memory errors (2002)
Working Paper: On robust local polynomial estimation with long-memory errors (2000)
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