Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets
Keyword(s):
Abstract The asymptotic theory for the memory-parameter estimator constructed from the log-regression with wavelets is incomplete for 1/$f$ processes that are not necessarily Gaussian or linear. Having a complete version of this theory is necessary because of the importance of non-Gaussian and non-linear long-memory models in describing financial time series. To bridge this gap, we prove that, under some mild assumptions, a newly designed memory estimator, named LRMW in this paper, is asymptotically consistent. The performances of LRMW in three simulated long-memory processes indicate the efficiency of this new estimator.
2002 ◽
Vol 16
(2)
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pp. 101-111
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2020 ◽
Vol 49
(2)
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pp. 578-595
Keyword(s):
2005 ◽