Arbeitspapier
On robust local polynomial estimation with long-memory errors
Prediction in time series models with a trend requires reliable estima- tion 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-estimators are asymptotically equivalent to the least square solution, under the (ideal) Gaussian model. Outliers turn out to have a major effect on nonrobust bandwidht selection, in particular due to the change of the dependence structure.
- Language
-
Englisch
- Bibliographic citation
-
Series: CoFE Discussion Paper ; No. 00/18
- Classification
-
Wirtschaft
- Subject
-
Zeitreihenanalyse
Nichtparametrisches Verfahren
Robustes Verfahren
Theorie
Statistischer Fehler
- Event
-
Geistige Schöpfung
- (who)
-
Beran, Jan
Feng, Yuanhua
Gosh, Sucharita
Sibbertsen, Philipp
- Event
-
Veröffentlichung
- (who)
-
University of Konstanz, Center of Finance and Econometrics (CoFE)
- (where)
-
Konstanz
- (when)
-
2000
- Handle
- URN
-
urn:nbn:de:bsz:352-opus-5226
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
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
- Gosh, Sucharita
- Sibbertsen, Philipp
- University of Konstanz, Center of Finance and Econometrics (CoFE)
Time of origin
- 2000
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