By Vasco Cúrdia, Marco Del Negro and Daniel Greenwald
We estimate a DSGE model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student-t distribution. Results from the Smets and Wouters (2007) model estimated on the usual set of macroeconomic time series over the 1964-2011 period indicate that the Student-t specification is strongly favored by the data even when we allow for low-frequency variation in the volatility of the shocks, and that the estimated degrees of freedom are quite low for several shocks that drive U.S. business cycles, implying an important role for rare large shocks. This result holds even if we exclude the Great Recession period from the sample. We also show that inference about low-frequency changes in volatility and in particular, inference about the magnitude of the Great Moderation is different once we allow for fat tails.
This paper shows that rare events matter, and so even if they do not appear in the data. This means a rejection of the common assumption that shocks are normally distributed. That, we knew already, one has simply to observe that recessions are shorter and steeper than booms. The merit of the paper is to show that the simplifying assumption of normally distributed shocks matters in modelling. What this means in terms of policy remains to be seen.