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Accurate forecasting of the U.K. gross value added (GVA) is fundamental formeasuring 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 toinflation. Transformed or inflation-adjusted series can still be challengingfor classical stationarity-assuming forecasters. We adopt a different approachthat works directly with the GVA series by advancing recent forecasting methodsfor locally stationary time series. Our approach results in more accurate andreliable forecasts, and continues to work well even when the ABML seriesbecomes highly variable during the COVID pandemic.

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