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Locally stationary long memory estimation

Abstract : There exists a wide literature on modelling strongly dependent time series using a longmemory parameter d, including more recent work on semiparametric wavelet estimation. As a generalization of these latter approaches, in this work we allow the long-memory parameter d to be varying over time. We embed our approach into the framework of locally stationary processes. We show weak consistency and a central limit theorem for our log-regression wavelet estimator of the time-dependent d in a Gaussian context. Both simulations and a real data example complete our work on providing a fairly general approach.
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Contributor : François Roueff Connect in order to contact the contributor
Submitted on : Thursday, April 8, 2010 - 4:40:03 PM
Last modification on : Friday, October 1, 2021 - 9:54:07 AM
Long-term archiving on: : Thursday, September 23, 2010 - 12:16:19 PM


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François Roueff, Rainer von Sachs. Locally stationary long memory estimation. Stochastic Processes and their Applications, Elsevier, 2011, 121 (4), pp.Pages 813-844. ⟨10.1016/j.spa.2010.12.004⟩. ⟨hal-00408224v2⟩



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