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Forecasting Long Memory Series Subject to Structural Change: A Two-Stage Approach

Gustavo Fruet Dias () and Fotis Papailias ()
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Gustavo Fruet Dias: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Fotis Papailias: Queen's University Belfast and quantf Research, Postal: Queen's University Management School, Queen's University Belfast, Riddel Hall, 185 Stranmillis Road, BT9 5EE, Northern Ireland

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the 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 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.

Keywords: Forecasting; Spurious Long Memory; Structural Change; Local Whittle (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Date: 2014-12-15
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