Publication: Out-of-sample forecast errors in misspecified perturbed long memory processes
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1998-07
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Abstract
The correlogram is not a useful diagnosis tool in the presence of long-memory or long range depedent time
series. The aim of this paper is to illustrate this claim by examining the relative increase in mean square
forecast error from fitting a weakly stationary process to the series of interest hen in fact the true model is a
so-called perturbed long-memory process recently introduced by Granger and Marmol (1997). This model has
the property of being unidentifiable from a white noise process on the basis of the correlogram and the usual
rule-of thumbs in the Box-Jenkins methodology. We prove that this kind of misspecification can lead to
serious errors in terms of forecasting.
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Perturbed long-memory, Correlogram, Forecast error