Arbeitspapier
On robust local polynominal estimation with long-memory errors
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.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2000,35
- Subject
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Zeitreihenanalyse
Nichtparametrisches Verfahren
Robustes Verfahren
Theorie
Statistischer Fehler
- Event
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Geistige Schöpfung
- (who)
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Beran, Jan
Feng, Yuanhua
Ghosh, Sucharita
Sibbertsen, Philipp
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2000
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Beran, Jan
- Feng, Yuanhua
- Ghosh, Sucharita
- Sibbertsen, Philipp
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2000
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