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Out-of-sample forecast errors in misspecific perturbed long memory processes

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  • Miguel Arranz
  • Francesc Marmol

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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  • Miguel Arranz & Francesc Marmol, 2001. "Out-of-sample forecast errors in misspecific perturbed long memory processes," Statistical Papers, Springer, vol. 42(4), pages 423-436, October.
    • Handle: RePEc:spr:stpapr:v:42:y:2001:i:4:p:423-436
    DOI: 10.1007/s003620100071
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    4. Andersson, Michael K., 1998. "On the Effects of Imposing or Ignoring Long Memory when Forecasting," SSE/EFI Working Paper Series in Economics and Finance 225, Stockholm School of Economics.
    5. Granger, C.W.J. (Clive William John) & Marmol, Francesc, 1998. "The correlogram of a long memory process plus a simple noise," DES - Working Papers. Statistics and Econometrics. WS 9820, Universidad Carlos III de Madrid. Departamento de Estadística.
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