Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a method to allow usual business cycle models to account for the unusual COVID episode. The pandemic and the public and private responses to it are represented by a new shock called the Covid shock, which loads onto wedges that underlie the usual shocks and comes with news about its evolution. We apply our method to a standard medium-scale model, estimating the loadings with 2020q2 data and the evolving news using professional forecasts. The Covid shock accounts for most of the early macroeconomic dynamics, was inflationary and a persistent drag on activity, and the majority of its effects were unanticipated. We also show how the Covid shock can be used to estimate DSGE models with data before, during, and after the pandemic.