Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265845 
Year of Publication: 
2022
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2022-069/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for the quantiles of 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. First, a general economic uncertainty factor with a slight tilt toward financial conditions. Second, 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 often better forecasts are obtained using the National Financial Conditions Index (NFCI), the uncertainty factor models are superior when forecasting employment and in general the uncertainty factors have predictive content that is complementary to the NFCI
Subjects: 
Economic uncertainty
real-time forecasting
quantile forecasting
factor analysis
JEL: 
E27
C21
C38
Document Type: 
Working Paper

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