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 ; Gosh, Sucharita ; Sibbertsen, Philipp
Publisher:	Konstanz : University of Konstanz, Center of Finance and Econometrics (CoFE)
Subject:	Zeitreihenanalyse \| Nichtparametrisches Verfahren \| Theorie \| Statistischer Fehler

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Series:	CoFE Discussion Paper ; 00/19
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	870146467 [GVK] hdl:10419/85219 [Handle] RePEc:zbw:cofedp:0019 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010324084