Locally stationary long memory estimation
By Franccois Roueff, R. von Sachs. 2009
145 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/ee18965acdcb13072f2daee3f6021b59dcddd4e1
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Citations in the corpus
- Time-Frequency Dynamics of Biofuels-Fuels-Food System (2012, methodology): “ts of time series without losing the time information. Moreover, the wavelet methodology is constructed to work with nonstationary data, which is a frequent issue in the nancial time series modeling (Roue and Sachs, 2011). We are the rst ones to apply the wavelet coherence analysis on biofuels (etha”