Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/85219 
Year of Publication: 
2000
Series/Report no.: 
CoFE Discussion Paper No. 00/19
Publisher: 
University of Konstanz, Center of Finance and Econometrics (CoFE), Konstanz
Abstract: 
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.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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