Optimal prediction with nonstationary ARFIMA model

Mohamed Boutahar

Journal of Forecasting, 2007, vol. 26, issue 2, 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.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:26:y:2007:i:2:p:95-111

DOI: 10.1002/for.1012

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