Arbeitspapier
Nonparametric M-Estimation with Long-Memory Errors
We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example.
- Sprache
-
Englisch
- Erschienen in
-
Series: CoFE Discussion Paper ; No. 00/19
- Klassifikation
-
Wirtschaft
- Thema
-
Zeitreihenanalyse
Nichtparametrisches Verfahren
Theorie
Statistischer Fehler
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Beran, Jan
Gosh, Sucharita
Sibbertsen, Philipp
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Konstanz, Center of Finance and Econometrics (CoFE)
- (wo)
-
Konstanz
- (wann)
-
2000
- Handle
- URN
-
urn:nbn:de:bsz:352-opus-5199
- 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
- Gosh, Sucharita
- Sibbertsen, Philipp
- University of Konstanz, Center of Finance and Econometrics (CoFE)
Entstanden
- 2000
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