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Beran, Jan
Gosh, Sucharita
Sibbertsen, Philipp
Year of Publication: 
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CoFE Discussion Paper 00/19
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.
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Working Paper

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