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On the Effects of Imposing or Ignoring Long Memory when Forecasting

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  • Andersson, Michael K.

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

Since the true nature of a time series process is often unknown it is important to understand the effects of model choice. This paper examines how the choice between modelling stationary time series as ARMA or ARFIMA processes affects the accuracy of forecasts. This is done, for first-order autoregressions and moving averages and for ARFIMA 1,d,0) processes, by means of a Monte Carlo simulation study. The fractional models are estimated using the technique of Geweke and Porter-Hudak, the modified rescaled range and the maximum likelihood procedure. We conclude that ignoring long memory is worse than imposing it, when forecasting, and that the ML estimator is preferred.

Suggested Citation

  • 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.
    • Handle: RePEc:hhs:hastef:0225
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    Citations

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    Cited by:

    1. 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.
    2. Ellis, Craig & Wilson, Patrick, 2004. "Another look at the forecast performance of ARFIMA models," International Review of Financial Analysis, Elsevier, vol. 13(1), pages 63-81.
    3. Chaker Aloui, 2003. "Long-Range Dependence in Daily Volatility on Tunisian Stock Market," Working Papers 0340, Economic Research Forum, revised Dec 2003.

    More about this item

    Keywords

    ARFIMA; fractional integration; periodogram regression; rescaled range; maximum likelihood; forecast error;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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