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Measuring Uncertainty and Its Effects in the COVID-19 Era

We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. We also consider an extended version of the model, based on a latent state approach to accommodating outliers in volatility, to reduce the influence of extreme observations from the COVID period. The estimates we obtain yield very large increases in macroeconomic and financial uncertainty over the course of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but with both models, the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method).

Keywords: Bayesian VARs, stochastic volatility, pandemics.
JEL classification codes: E32, E44, C11, C55.

Suggested citation: Carriero, Andrea, Todd E. Clark, Massimiliano Marcellino, and Elmar Mertens. 2020. “Measuring Uncertainty and Its Effects in the COVID-19 Era.” Federal Reserve Bank of Cleveland, Working Paper No. 20-32.


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