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Automatic locally stationary time series forecasting with application to predicting UK gross value added time series

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Automatic locally stationary time series forecasting with application to predicting UK gross value added time series. / Killick, Rebecca; Knight, Marina I; Nason, Guy P et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), 23.08.2024.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Killick, R, Knight, MI, Nason, GP, Nunes, MA & Eckley, IA 2024, 'Automatic locally stationary time series forecasting with application to predicting UK gross value added time series', Journal of the Royal Statistical Society: Series C (Applied Statistics). https://doi.org/10.1093/jrsssc/qlae043

APA

Killick, R., Knight, M. I., Nason, G. P., Nunes, M. A., & Eckley, I. A. (2024). Automatic locally stationary time series forecasting with application to predicting UK gross value added time series. Journal of the Royal Statistical Society: Series C (Applied Statistics). Advance online publication. https://doi.org/10.1093/jrsssc/qlae043

Vancouver

Killick R, Knight MI, Nason GP, Nunes MA, Eckley IA. Automatic locally stationary time series forecasting with application to predicting UK gross value added time series. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2024 Aug 23. Epub 2024 Aug 23. doi: 10.1093/jrsssc/qlae043

Author

Killick, Rebecca ; Knight, Marina I ; Nason, Guy P et al. / Automatic locally stationary time series forecasting with application to predicting UK gross value added time series. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2024.

Bibtex

@article{b6227ea20ebf4e8a8cad6f075da43108,
title = "Automatic locally stationary time series forecasting with application to predicting UK gross value added time series",
abstract = "Accurate forecasting of the UK gross value added (GVA) is fundamental for measuring the growth of the UK economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.",
author = "Rebecca Killick and Knight, {Marina I} and Nason, {Guy P} and Nunes, {Matthew A} and Eckley, {Idris A}",
year = "2024",
month = aug,
day = "23",
doi = "10.1093/jrsssc/qlae043",
language = "English",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",

}

RIS

TY - JOUR

T1 - Automatic locally stationary time series forecasting with application to predicting UK gross value added time series

AU - Killick, Rebecca

AU - Knight, Marina I

AU - Nason, Guy P

AU - Nunes, Matthew A

AU - Eckley, Idris A

PY - 2024/8/23

Y1 - 2024/8/23

N2 - Accurate forecasting of the UK gross value added (GVA) is fundamental for measuring the growth of the UK economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.

AB - Accurate forecasting of the UK gross value added (GVA) is fundamental for measuring the growth of the UK economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.

U2 - 10.1093/jrsssc/qlae043

DO - 10.1093/jrsssc/qlae043

M3 - Journal article

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

ER -