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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)

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    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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    File URL: http://hdl.handle.net/10.1002/for.1012
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    Bibliographic Info

    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:95-111

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009. "Forecasting long memory time series under a break in persistence," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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