Journal
STOCHASTIC PROCESSES AND THEIR APPLICATIONS
Volume 121, Issue 4, Pages 813-844Publisher
ELSEVIER
DOI: 10.1016/j.spa.2010.12.004
Keywords
Locally stationary process; Long memory; Semi-parametric estimation; Wavelets
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Funding
- Fondation Telecom
- Belgian government (Belgian Science Policy) [P6/03]
- Communaute francaise de Belgique [07/12-002]
- Academic universitaire Louvain
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There exists a wide literature on parametrically or semi-parametrically modelling strongly dependent time series using a long-memory parameter d, including more recent work on 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 adopt a semi-parametric approach in order to avoid fitting a time-varying parametric model, such as tvARFIMA, to the observed data. We study the asymptotic behavior of a local log-regression wavelet estimator of the time-dependent d. Both simulations and a real data example complete our work on providing a fairly general approach. (C) 2010 Elsevier B.V. All rights reserved.
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