Estimating macroeconomic uncertainty and discord using info-metrics

We apply generalized beta and triangular distributions to histograms from the Survey of Professional Forecasters (SPF) to estimate forecast uncertainty, shocks and discord using information framework, and compare these with moment-based estimates. We find these two approaches to produce analogous results, except in cases where the underlying densities deviate significantly from normality. Even though the Shannon entropy is more inclusive of different facets of a forecast density, we find that with SPF forecasts it is largely driven by the variance of the densities. We use Jenson-Shannon Information to measure ex ante “news” or “uncertainty shocks” in real time, and find that this ‘news’ is closely related to revisions in forecast means, countercyclical, and raises uncertainty. Using standard vector auto-regression analysis, we confirm that uncertainty affects the economy negatively.