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The onset of the COVID-19 pandemic and the great lockdown caused macroeconomic variables to display complex patterns that hardly follow any historical behavior. In the context of Bayesian VARs, an off-the-shelf exercise demonstrates how a very low number of extreme pandemic observations bias the estimated persistence of the variables, affecting forecasts and giving a myopic view of the economic effects after a structural shock. I propose an easy and straightforward solution to deal with these extreme episodes, as an extension of the Minnesota Prior with dummy observations by allowing for time dummies. The Pandemic Priors succeed in recovering these historical relationships and the proper identification and propagation of structural shocks.
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