“A New Way to Quantify the Effect of Uncertainty“
with Alex Richter
FRB Dallas Working Paper 1705, February 2018

This paper develops a new way to quantify the effects of aggregate uncertainty that accounts for exogenous and endogenous sources. First, we use Bayesian methods to estimate a nonlinear New Keynesian model with stochastic volatility and a zero lower bound constraint on the nominal interest rate. We discipline the model by matching data on uncertainty, in addition to common macro time series. Second, we use the Euler equation to decompose output into expected output and the expected variance and skewness of output. We then filter a time series for each term. Our method captures the effects of higher-order moments over horizons beyond 1 quarter by recursively decomposing expected output. Over a 1-quarter horizon, output uncertainty reduced output less than 0.01% every quarter, similar to volatility shocks in our model. Over horizons that remove the influence of expected output, output uncertainty on average reduced output 0.06% and the peak effect was 0.15% during the Great Recession, similar to structural VAR estimates. Other higher-order moments had much smaller effects on output.