Nowcasting Unemployment Insurance Claims in the Time of COVID-19

Near term forecasts, also called nowcasts, are most challenging but also most important
when the economy experiences an abrupt change. In this paper, we explore the
performance of models with different information sets and data structures in order to best
nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19
pandemic. We show that the best model, particularly near the structural break in claims,
is a state-level panel model that includes dummy variables to capture the variation in
timing of state-of-emergency declarations. Autoregressive models perform poorly at first
but catch up relatively quickly. Models including Google Trends are outperformed by
alternative models in nearly all periods. Our results suggest that in times of structural
change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant
information in the cross sectional dimension improve forecasts, but in later periods the
efficiency of autoregressive models dominates.