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.
- Language
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Englisch
- Bibliographic citation
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Series: CoFE Discussion Paper ; No. 00/19
- Classification
-
Wirtschaft
- Subject
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Zeitreihenanalyse
Nichtparametrisches Verfahren
Theorie
Statistischer Fehler
- Event
-
Geistige Schöpfung
- (who)
-
Beran, Jan
Gosh, Sucharita
Sibbertsen, Philipp
- Event
-
Veröffentlichung
- (who)
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University of Konstanz, Center of Finance and Econometrics (CoFE)
- (where)
-
Konstanz
- (when)
-
2000
- Handle
- URN
-
urn:nbn:de:bsz:352-opus-5199
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Beran, Jan
- Gosh, Sucharita
- Sibbertsen, Philipp
- University of Konstanz, Center of Finance and Econometrics (CoFE)
Time of origin
- 2000