Testing Hybrid Forecasts for Imports and Exports

The quality of economic forecasts tends to deteriorate during times of stress such as the COVID-19 pandemic, raising questions about how to improve forecasts during exceptional times. One method of forecasting that has received less attention is refining model-based forecasts with judgmental adjustment, or hybrid forecasting. Judgmental adjustment is the process of incorporating information from outside a model into a forecast or adjusting a forecast subjectively. Hybrid forecasts could be particularly useful during extraordinary times such as the COVID-19 pandemic, as models that do not incorporate judgment may be less able to adapt to a rapidly changing environment. In this article, Thomas Cook, Amaze Lusompa, and Johannes Matschke explore how hybrid forecasts performed relative to pure model-based forecasts during the COVID-19 pandemic, using forecasts for imports and exports as a test. They find that hybrid models improved import and export forecasts during the trough of the pandemic but did not materially improve forecasts during the pandemic recovery or over longer forecasting horizons. They conclude that hybrid forecasts are mainly helpful for near-term forecasts during extraordinary circumstances.