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.
Authors
- Idris A. Eckley (ORCID: https://orcid.org/0000-0001-9957-2460)
- Matthew A. Nunes (ORCID: https://orcid.org/0000-0002-4719-2690)
- Marina I. Knight (ORCID: https://orcid.org/0000-0001-9926-6092)
- Rebecca Killick (ORCID: https://orcid.org/0000-0003-0583-3960)
- Guy P. Nason (ORCID: https://orcid.org/0000-0002-4664-3154)
Institutions
- NIHR Imperial Biomedical Research Centre (GB)
- Imperial College London (GB)
- University of Bath (GB)
- University of York (GB)
- Lancaster University (GB)
Publication Details
- Journal
- Journal of the Royal Statistical Society Series C (Applied Statistics)
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1093/jrsssc/qlae043
- Citations
- 3
- Primary Topic
- Forecasting Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- Research Councils UK
- Directorate for Biological Sciences
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council
- Economic and Social Research Council
- Natural Environment Research Council
- Science and Technology Facilities Council