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    Long memory or shifting means in geophysical time series?

    Access Status
    Fulltext not available
    Authors
    Rea, W.
    Reale, M.
    Brown, J.
    Oxley, Leslie
    Date
    2011
    Type
    Conference Paper
    
    Metadata
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    Citation
    Rea, W. and Reale, M. and Brown, J. and Oxley, L. 2011. Long memory or shifting means in geophysical time series? Mathematics and Computers in Simulation. 81 (7): pp. 1441-1453.
    Source Title
    Mathematics and Computers in Simulation
    DOI
    10.1016/j.matcom.2010.06.007
    ISSN
    0378-4754
    School
    School of Economics and Finance
    URI
    http://hdl.handle.net/20.500.11937/48365
    Collection
    • Curtin Research Publications
    Abstract

    In the literature many papers state that long-memory time series models such as Fractional Gaussian Noises (FGN) or Fractionally Integrated series (FI(d)) are empirically indistinguishable from models with a non-stationary mean, but which are mean reverting. We present an analysis of the statistical cost of model mis-specification when simulated long memory series are analysed by Atheoretical Regression Trees (ART), a structural break location method. We also analysed three real data sets, one of which is regarded as a standard example of the long memory type. We find that FGN and FI(d) processes do not account for many features of the real data. In particular, we find that the data sets are not H-self-similar. We believe the data sets are better characterized by non-stationary mean models. © 2010 IMACS. Published by Elsevier B.V. All rights reserved.

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