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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Technical Report ; No. 2000,35
- Thema
-
Zeitreihenanalyse
Nichtparametrisches Verfahren
Robustes Verfahren
Theorie
Statistischer Fehler
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Beran, Jan
Feng, Yuanhua
Ghosh, Sucharita
Sibbertsen, Philipp
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (wo)
-
Dortmund
- (wann)
-
2000
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Beran, Jan
- Feng, Yuanhua
- Ghosh, Sucharita
- Sibbertsen, Philipp
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Entstanden
- 2000
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