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.

翻译：准确预测英国总增加值（GVA）是衡量英国经济增长的基础。GVA数据（如ABML序列）中常见的非平稳性表现为因通货膨胀导致方差随时间递增。即使经过变换或通胀调整后的序列，对于依赖平稳性假设的传统预测方法而言仍具有挑战性。我们采用不同方法直接处理GVA序列，通过推进局部平稳时间序列的最新预测技术来实现预测。我们的方法能提供更准确可靠的预测，即使在COVID疫情期间ABML序列呈现高度波动时仍表现良好。