TY - JOUR

T1 - Forecasting long memory series subject to structural change: A two-stage approach

AU - Papailias, Fotis

AU - Dias, Gustavo Fruet

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1016/j.ijforecast.2015.01.006

DO - 10.1016/j.ijforecast.2015.01.006

M3 - Journal article

VL - 31

SP - 1056

EP - 1066

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -