Out-of-sample forecast errors in misspecified perturbed long memory processes
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.
|Date of creation:||Jul 1998|
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- Smith, Jeremy & Taylor, Nick & Yadav, Sanjay, 1995. "Comparing the Bias and Misspecification in Arfima Models," The Warwick Economics Research Paper Series (TWERPS) 442, University of Warwick, Department of Economics.
- Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
- 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.
- Smith, Jeremy & Yadav, Sanjay, 1994. "Forecasting costs incurred from unit differencing fractionally integrated processes," International Journal of Forecasting, Elsevier, vol. 10(4), pages 507-514, December.
- Marmol, Francesc & Granger, C.W.J. (Clive William John), 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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