Business forecasting during the pandemic

Abstract

The COVID-19 pandemic shock represents a once-in-a-generation challenge to both the global economy and to business forecasting, contributing to elevated economic uncertainty through today. In this article, we perform a retrospective evaluation of some of the workhorse statistical models used by business economists to see which approaches were most resilient during the pandemic shock. We find projection-based approaches were more resilient to the pandemic shock than iteration-based forecasts in the cases we studied. We also find that the pandemic induced significant variation in forecast accuracy among the models which incorporate macroeconomic data. Incorporating alternative high-frequency data which gained currency during the pandemic into these models did not necessarily improve forecast performance, however more research is needed to assess the extent to which these indicators improved business planning.