报告题目：How Well Does Uncertainty Forecast Economic Activity?（不确定性预测经济活动靠谱吗？）

报告人：徐佳文（上海财经大学）

报告时间：2021年4月13日（星期二）上午10:30-11:45

报告地点：bat365中文官方网站大楼218会议室

邀请部门：经济学系

报告人简介：上海财经大学高等研究院助理教授，本科毕业于上海财经大学，2013年于波士顿大学获得经济学博士学位。研究兴趣为计量经济学、时间序列分析、宏观经济学。论文发表于International Journal of Forecasting、Applied Economics、Economic Modelling等期刊。

报告摘要：

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.