Nonparametric M-Estimation with long-memory errors

dc.contributor.authorBeran, Jan
dc.contributor.authorGhosh, Sucharitadeu
dc.contributor.authorSibbertsen, Philippdeu
dc.date.accessioned2011-03-22T17:45:37Zdeu
dc.date.available2011-03-22T17:45:37Zdeu
dc.date.issued2000deu
dc.description.abstractWe investigate the behavior of nonparametric kernel M-estimator in the presence of long-memory errors. The optimal bandwidth and 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.eng
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dc.titleNonparametric M-Estimation with long-memory errorseng
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@techreport{Beran2000Nonpa-722,
  year={2000},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={Nonparametric M-Estimation with long-memory errors},
  number={2000/19},
  author={Beran, Jan and Ghosh, Sucharita and Sibbertsen, Philipp}
}
kops.citation.iso690BERAN, Jan, Sucharita GHOSH, Philipp SIBBERTSEN, 2000. Nonparametric M-Estimation with long-memory errorsdeu
kops.citation.iso690BERAN, Jan, Sucharita GHOSH, Philipp SIBBERTSEN, 2000. Nonparametric M-Estimation with long-memory errorseng
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