On robust local polynomial estimation with long-memory errors
AbstractPrediction 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. --
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 18 (2002)
Issue (Month): 2 ()
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Other versions of this item:
- Jan Beran & Yuanhua Feng & Sucharita Gosh & Philipp Sibbertsen, 2000. "On robust local polynomial estimation with long-memory errors," CoFE Discussion Paper 00-18, Center of Finance and Econometrics, University of Konstanz.
- Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2000. "On robust local polynominal estimation with long-memory errors," Technical Reports 2000,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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- Jan Beran & Sucharita Gosh & Philipp Sibbertsen, 2000.
"Nonparametric M-Estimation with Long-Memory Errors,"
CoFE Discussion Paper
00-19, Center of Finance and Econometrics, University of Konstanz.
- Beran, Jan & Ghosh, Sucharita & Sibbertsen, Philipp, 2000. "Nonparametric M-estimation with long-memory errors," Technical Reports 2000,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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