Modeling Macroeconomic Variations after Covid-19

The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-\covid\ forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use \covid\ indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and \covid\ uncertainty has receded. Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling \covid\ and treating it as exogenous. These responses to economic shocks are distinctly different from those to a \covid\ shock, and distinguishing between the two types of shocks can be important in macroeconomic modeling post-\covid.