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
Englisch

Bibliographic citation
Series: CoFE Discussion Paper ; No. 00/19

Classification
Wirtschaft
Subject
Zeitreihenanalyse
Nichtparametrisches Verfahren
Theorie
Statistischer Fehler

Event
Geistige Schöpfung
(who)
Beran, Jan
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-5199
Last update
10.03.2025, 11:43 AM CET

Data provider

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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
  • Gosh, Sucharita
  • Sibbertsen, Philipp
  • University of Konstanz, Center of Finance and Econometrics (CoFE)

Time of origin

  • 2000

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