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.

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Year of publication:	2000
Authors:	Beran, Jan ; Ghosh, Sucharita ; Sibbertsen, Philipp
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Zeitreihenanalyse \| Nichtparametrisches Verfahren \| Theorie \| Statistischer Fehler

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Series:	Technical Report ; 2000,36
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	757657109 [GVK] hdl:10419/77191 [Handle] RePEc:zbw:sfb475:200036 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010316534