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Measuring Uncertainty and Its Effects in the COVID-19 Era


We measure the effects of the COVID-19 outbreak on uncertainty, and we assess the consequences of the uncertainty 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. 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. The model includes additional latent volatility states in order to accommodate outliers in volatility, to reduce the influence of extreme observations from the COVID period. Our estimates yield large increases in macroeconomic and financial uncertainty since the onset of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but 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. 2022. "Measuring Uncertainty and Its Effects in the COVID-19 Era." Working Paper No. 20-32R. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-wp-202032r.

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