Does economic uncertainty predict real activity in real time?

Abstract

We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for The Conference Board’s coincident economic index and its components (industrial production, employment, personal income, and manufacturing and trade sales). The results show that the measures hold (real-time) predictive power for quantiles in the left tail. Because uncertainty measures are all proxies of an unobserved entity, we combine their information using principal component analysis. A large fraction of the variance of the uncertainty measures can be explained by two factors: a general economic uncertainty factor with a slight tilt toward financial conditions, and a consumer/media confidence index which remains elevated after recessions. Using a predictive regression model with the factors from the set of uncertainty measures yields more consistent gains compared to a model with an individual uncertainty measure. Further, although accurate forecasts are obtained using the National Financial Conditions Index (NFCI), the uncertainty factor models are better when forecasting employment, and in general, the uncertainty factors have predictive content that is complementary to the NFCI.