Reporter: Dr. Xu Jiawen, Assistant Professor of the Advanced Research Institute of Shanghai University of Finance and Economics. She graduated from Shanghai University of Finance and Economics with a bachelor's degree and obtained a PhD in economics from Boston University in 2013. Her research interests are Econometrics, Time Series Analysis, and Macroeconomics. Her papers have been published in International Journal of Forecasting, Applied Economics, Economic Modelling and other journals.

The Abstract of the Report:

Despite the enormous reach and influence of the literature on economic and economic policy uncertainty, the forecasting performance of economic uncertainty measures has been surprisingly under-researched. We evaluate the ability of several popular measures of uncertainty to forecast in-sample and out-of-sample over real and financial outcome variables, as well as over different quantiles of the GDP growth distribution. Real-time data and estimation considerations are highly consequential, owing to look-ahead bias. We construct new real-time versions of both macroeconomic (Jurado et al. (2015)) and financial uncertainty (Luvigson et al (forthcoming)), and analyze them together with their ex-post counterparts. We find some explanatory power in all uncertainty measures, with relatively good performance by ex-post macroeconomic uncertainty (MU), which has additional in-sample predictive content over the widely-used excess bond premium of Gilchrist and Zakrajsek (2012) and the National Financial Conditions Index (NFCI). However, real-time MU performs poorly compared to its ex-post counterpart, a finding that we relate to sub-sample instability in the performance of ex-post MU.