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.
Year of publication: |
2000
|
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Authors: | Beran, Jan ; Ghosh, Sucharita ; Sibbertsen, Philipp |
Institutions: | Institut für Wirtschafts- und Sozialstatistik, Universität Dortmund |
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