報告題目：How Well Does Uncertainty Forecast Economic Activity?（不確定性預測經濟活動靠譜嗎🙍✬？）

報告人☎：徐佳文（上海財經大學）

報告時間：2021年4月13日（星期二）上午10:30-11:45

報告地點：商學院大樓218會議室

邀請部門🧎‍♀️：經濟學系

報告人簡介：上海財經大學高等研究院助理教授🤹🏼‍♂️，本科畢業於上海財經大學，2013年於波士頓大學獲得經濟學博士學位。研究興趣為計量經濟學、時間序列分析、宏觀經濟學。論文發表於International Journal of Forecasting💆🏻‍♀️🤘🏻、Applied Economics、Economic Modelling等期刊。

報告摘要：

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.