Working Paper

How Well Does Economic Uncertainty Forecast Economic Activity?

Abstract: Despite the enormous reach and influence of the literature on economic and economic policy uncertainty, one surprisingly under-researched topic has been the forecasting performance of economic uncertainty measures. We evaluate the ability of seven popular measures of uncertainty to forecast in-sample and out-of-sample over real and financial outcome variables. We also evaluate predictive content over different quantiles of the GDP growth distribution. Real-time data and estimation considerations are highly consequential, and we devote considerable attention to them. Four main findings emerge. First, there is some explanatory power in all uncertainty measures, with relatively good performance by macroeconomic uncertainty (Jurado, Ludvigson, and Ng, 2015). Second, macro uncertainty has additional predictive content over the widely-used excess bond premium of (Gilchrist and Zakrajsek, 2012) and the National Financial Conditions Index. Third, quantile regressions for GDP growth indicate strong predictive power, especially at the lower ends of the distribution, for all uncertainty measures except the VIX. Finally, we construct new real-time versions of both macroeconomic and financial uncertainty and compare them to their ex-post counterparts used in the literature. Real-time uncertainty measures have comparatively poor forecasting performance, even to the point of overturning some of the conclusions that emerge from using ex-post uncertainty measures.

Keywords: Forecasting; Uncertainty; Factor model; Real-time data; Quantile regression;

JEL Classification: C22; C53;

https://doi.org/10.17016/FEDS.2019.085

Bibliographic Information

Provider: Board of Governors of the Federal Reserve System (U.S.)

Part of Series: Finance and Economics Discussion Series

Publication Date: 2019-12-16

Number: 2019-085