Modeling Macroeconomic Variations After COVID-19

TL;DR

This paper examines how COVID-19 has altered macroeconomic data properties, proposing methods to disentangle pandemic-related shocks from traditional economic shocks for improved modeling and forecasting.

Contribution

It introduces a framework using COVID indicators as controls to separate pandemic shocks from economic shocks in macroeconomic models.

Findings

Economic uncertainty remained high at end of 2020 despite recovery.

Modeling COVID as an exogenous shock helps recover pre-pandemic dynamic responses.

COVID shocks are larger but shorter-lived compared to economic shocks.

Abstract

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 which are much larger but shorter lived. Disentangling the two types of shocks can be important in macroeconomic modeling post-covid.