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Optimal prediction with nonstationary ARFIMA model

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  • Mohamed Boutahar

    (GREQAM, and Department of Mathematics, Luminy Faculty of Sciences, Marseille, France)

Abstract

We propose two methods to predict nonstationary long-memory time series. In the first one we estimate the long-range dependent parameter d by using tapered data; we then take the nonstationary fractional filter to obtain stationary and short-memory time series. In the second method, we take successive differences to obtain a stationary but possibly long-memory time series. For the two methods the forecasts are based on those obtained from the stationary components. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Mohamed Boutahar, 2007. "Optimal prediction with nonstationary ARFIMA model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 95-111.
    • Handle: RePEc:jof:jforec:v:26:y:2007:i:2:p:95-111
    DOI: 10.1002/for.1012
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    References listed on IDEAS

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    1. Velasco, Carlos, 1999. "Non-stationary log-periodogram regression," Journal of Econometrics, Elsevier, vol. 91(2), pages 325-371, August.
    2. M. S. Peiris & B. J. C. Perera, 1988. "On Prediction With Fractionally Differenced Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(3), pages 215-220, May.
    3. Christos Agiakloglou & Paul Newbold & Mark Wohar, 1993. "Bias In An Estimator Of The Fractional Difference Parameter," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 235-246, May.
    4. Robinson, Peter M. & Velasco, Carlos, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
    5. Peter M Robinson & Carlos Velasco, 2000. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in Journal of the American Statistical Association, 95, (2000), pp.1229-1243.)," STICERD - Econometrics Paper Series 391, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    Cited by:

    1. Florian Heinen & Philipp Sibbertsen & Robinson Kruse, 2009. "Forecasting long memory time series under a break in persistence," CREATES Research Papers 2009-53, Department of Economics and Business Economics, Aarhus University.
    2. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.

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