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Nonparametric M-estimation with long-memory errors

Authors
Journal
Journal of Statistical Planning and Inference
0378-3758
Publisher
Elsevier
Publication Date
Volume
117
Issue
2
Identifiers
DOI: 10.1016/s0378-3758(02)00391-9
Disciplines
  • Mathematics

Abstract

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.

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