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

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Standard

Forecasting long memory series subject to structural change: A two-stage approach. / Papailias, Fotis; Dias, Gustavo Fruet.

In: International Journal of Forecasting, Vol. 31, No. 4, 2015, p. 1056–1066.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Papailias, F & Dias, GF 2015, 'Forecasting long memory series subject to structural change: A two-stage approach', International Journal of Forecasting, vol. 31, no. 4, pp. 1056–1066. https://doi.org/10.1016/j.ijforecast.2015.01.006

APA

Papailias, F., & Dias, G. F. (2015). Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31(4), 1056–1066. https://doi.org/10.1016/j.ijforecast.2015.01.006

CBE

Papailias F, Dias GF. 2015. Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting. 31(4):1056–1066. https://doi.org/10.1016/j.ijforecast.2015.01.006

MLA

Papailias, Fotis and Gustavo Fruet Dias. "Forecasting long memory series subject to structural change: A two-stage approach". International Journal of Forecasting. 2015, 31(4). 1056–1066. https://doi.org/10.1016/j.ijforecast.2015.01.006

Vancouver

Papailias F, Dias GF. Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting. 2015;31(4):1056–1066. doi: 10.1016/j.ijforecast.2015.01.006

Author

Papailias, Fotis ; Dias, Gustavo Fruet. / Forecasting long memory series subject to structural change: A two-stage approach. In: International Journal of Forecasting. 2015 ; Vol. 31, No. 4. pp. 1056–1066.

Bibtex

@article{c383c8134f6741dfa8270a5bfdd28f24,
title = "Forecasting long memory series subject to structural change: A two-stage approach",
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.",
author = "Fotis Papailias and Dias, {Gustavo Fruet}",
year = "2015",
doi = "10.1016/j.ijforecast.2015.01.006",
language = "English",
volume = "31",
pages = "1056–1066",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",
number = "4",

}

RIS

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 -