  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.
