Abstract
Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. 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.
| Original language | English |
|---|---|
| Article number | qlae043 |
| Pages (from-to) | 18-33 |
| Journal | Journal of the Royal Statistical Society: Series C - Applied Statistics |
| Volume | 74 |
| Issue number | 1 |
| Early online date | 23 Aug 2024 |
| DOIs | |
| Publication status | Published - 31 Jan 2025 |
Data Availability Statement
The ABML data can be obtained from the Office for National Statistics website https://www.ons.gov.uk. The (differenced) ABML dataset for the period examined in this article, as well as the forecasting method, is available within the forecast LSW R package on CRAN.Funding
The authors were partially supported by the Research Councils UK (RCUK) Energy Programme. The Energy Programme is an RCUK cross-council initiative led by Engineering and Physical Sciences Research Council (EPSRC) and contributed to by Economic and Social Research Council (ESRC), Natural Environment Research Council (NERC), Biotechnology and Biological Sciences Research Council (BBSRC), and Science and Technology Facilities Council (STFC). G.P.N. gratefully acknowledges support from EPSRC grants EP/I01697X/1 and K020951/1. I.A.E. gratefully acknowledges support from EPSRC grant EP/I016368/1.