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Optimal prediction with nonstationary ARFIMA model Author info | Abstract | Publisher info | Download info | Related research | Statistics Mohamed Boutahar (GREQAM, and Department of Mathematics, Luminy Faculty of Sciences, Marseille, France)
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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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting .
Volume (Year): 26 (2007)
Issue (Month): 2 ()
Pages: 95-111
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Handle: RePEc:jof:jforec:v:26:y:2007:i:2:p:95-111Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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