Optimal prediction with nonstationary ARFIMA model

Mohamed Boutahar
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Mohamed Boutahar: GREQAM, and Department of Mathematics, Luminy Faculty of Sciences, Marseille, France, Postal: GREQAM, and Department of Mathematics, Luminy Faculty of Sciences, Marseille, France

Journal of Forecasting, 2007, vol. 26, issue 2, pages 95-111

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.

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