Pandemic Priors BVAR

Pandemic Priors: a simple, easy, and flexible way of estimating Bayesian VARs taking into consideration the pandemic period, as a Minnesota prior with time dummies

Update (Feb 2023): new fully revised version of the paper, including a test for the optimal level of shrinkage for the pandemic period and a test for suitability of the Pandemic Priors - Link here!

Update (Nov 2022): added the flexibility of how much signal to take from pandemic period

Paper: Pandemic Priors

Abstract:

The onset of the COVID-19 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 distorts 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 by allowing for time dummies. The method is flexible enough to let the econometrician optimally define the level of shrinkage, or arbitrarily choose how much signal to take from these extreme observations, nesting the boundary cases of an uninformative prior that soaks all the variance and a traditional Minnesota Prior. The Pandemic Priors succeed in recovering historical relationships and the proper identification and propagation of structural shocks.