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Nonparametric M-Estimation with Long-Memory Errors

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Author Info
Jan Beran () (Center of Finance and Econometrics)
Sucharita Gosh (Landscape Department, Swiss Federal Research Institute WSL)
Philipp Sibbertsen (Fachbereich Statistik, Universität Dortmund)

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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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Publisher Info
Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 00-19.

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Length: 9 Pages
Date of creation: Jun 2000
Date of revision:
Handle: RePEc:knz:cofedp:0019

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  1. Jan Beran, 1999. "SEMIFAR Models - A Semiparametric Framework for Modelling Trends, Long Range Dependence and Nonstationarity," CoFE Discussion Paper 99-16, Center of Finance and Econometrics, University of Konstanz. [Downloadable!]
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