Papailias, F. and Dias, G.F. (2015) Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31 (4). pp. 1056-1066. ISSN 0169-2070
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Elsevier. This is an author produced version of a paper subsequently published in International Journal of Forecasting. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Time series forecasting; Spurious long memory; Fractional integration; Local Whittle |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Apr 2019 09:31 |
Last Modified: | 09 Apr 2019 17:18 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijforecast.2015.01.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144633 |