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dc.contributor.authorRea, W.
dc.contributor.authorReale, M.
dc.contributor.authorBrown, J.
dc.contributor.authorOxley, Leslie
dc.identifier.citationRea, 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.

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.

dc.titleLong memory or shifting means in geophysical time series?
dc.typeConference Paper
dcterms.source.titleMathematics and Computers in Simulation
dcterms.source.seriesMathematics and Computers in Simulation
curtin.departmentSchool of Economics and Finance
curtin.accessStatusFulltext not available

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