Automatic locally stationary time series forecasting with application to predicting UK gross value added time series
File(s)
Published version
Author(s)
Killick, Rebecca
Knight, Marina Iuliana
Nason, Guy
Nunes, Matthew
Eckley, Idris
Type
Journal Article
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.
Date Issued
2025-01-01
Date Acceptance
2024-08-07
Citation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2025, 74 (1), pp.18-33
ISSN
0035-9254
Publisher
Oxford University Press
Start Page
18
End Page
33
Journal / Book Title
Journal of the Royal Statistical Society Series C: Applied Statistics
Volume
74
Issue
1
Copyright Statement
© The Royal Statistical Society 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlae043/7739728
Subjects
classical forecasting
COVID shocks
local partial autocorrelation
spectral estimation
wavelets
Publication Status
Published
Article Number
qlae043
Date Publish Online
2024-08-23