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Measuring Uncertainty and Its Impact on the Economy


We propose a new framework for measuring uncertainty and its effects on the economy, based on a large VAR model with errors whose stochastic volatility is driven by two common unobservable factors, representing aggregate macroeconomic and financial uncertainty. The uncertainty measures can also influence the levels of the variables so that, contrary to most existing measures, ours reflect changes in both the conditional mean and volatility of the variables, and their impact on the economy can be assessed within the same framework. Moreover, identification of the uncertainty shocks is simplified with respect to standard VAR-based analysis, in line with the FAVAR approach and with heteroskedasticity-based identification. Finally, the model, which is also applicable in other contexts, is estimated with a new Bayesian algorithm, which is computationally efficient and allows for jointly modeling many variables, while previous VAR models with stochastic volatility could only handle a handful of variables. Empirically, we apply the method to estimate uncertainty and its effects using US data, finding that there is indeed substantial commonality in uncertainty, sizable effects of uncertainty on key macroeconomic and financial variables with responses in line with economic theory.

Keywords: Bayesian VARs, stochastic volatility, large datasets.
JEL classification: E44, C11, C13, C33, C55.


Suggested citation: Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino, 2016. “Measuring Uncertainty and Its Impact on the Economy,” Federal Reserve Bank of Cleveland Working Paper, no. 16-22.

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