Forecasting long memory series subject to structural change: A two-stage approach

A two-stage forecasting approach for long memory time series is introduced. In the first step, we estimate the fractional exponent and, by applying the fractional differencing operator, obtain the underlying weakly dependent series. In the second step, we produce multi-step-ahead forecasts for the w...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 31; no. 4; pp. 1056 - 1066
Main Authors Papailias, Fotis, Fruet Dias, Gustavo
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier Sequoia S.A 01.10.2015
Subjects
Forecasting techniques
Simulation
Studies
Time series
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Summary:A two-stage forecasting approach for long memory time series is introduced. In the first step, we estimate the fractional exponent and, by applying the fractional differencing operator, obtain the underlying weakly dependent series. In the second step, we produce multi-step-ahead forecasts for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to both stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural change and yields good forecasting results.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2015.01.006