Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic

Killick, Rebecca and Knight, Marina I. and Nason, Guy P. and Nunes, Matthew A. and Eckley, Idris A. (2023) Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic. Other. UNSPECIFIED.

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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.

Item Type:
Monograph (Other)
Additional Information:
21 pages, 4 figures
Uncontrolled Keywords:
Research Output Funding/yes_externally_funded
Subjects:
?? stat.mestat.ap62m10, 91b84yes - externally funded ??
ID Code:
191311
Deposited By:
Deposited On:
26 May 2023 14:00
Refereed?:
No
Published?:
Published
Last Modified:
15 Jul 2024 08:01