题目：High-Dimensional VARs with Common Factors

主讲人：清华大学 苏良军教授

主持人：统计学院 常晋源教授

时间：2021年12月17日（周二）上午10:00-11:10

地点：腾讯会议，858 343 251

报告摘要：

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ₁-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for the estimates. A singular value thresholding procedure is used to determine the correct number of factors with probability approaching one. Both the LASSO estimator and the conservative LASSO estimator are employed to improve estimation precision. The conservative LASSO estimates of the non-zero coefficients are shown to be asymptotically equivalent to the oracle least squares estimates. Simulations demonstrate that our estimators perform reasonably well in finite samples given the complex high-dimensional nature of the model. In an empirical illustration we apply the methodology to explore dynamic connectedness in the volatilities of financial asset prices and the transmission of 'investor fear'. The findings reveal that a large proportion of connectedness is due to the common factors. Conditional on the presence of these common factors, the results still document remarkable connectedness due to the interactions between the individual variables, thereby supporting a common factor augmented VAR specification.

主讲人简介：

苏良军教授2004年取得加州大学San Diego分校经济学博士学位。2004-2008年在北京大学光华管理学院商务统计与计量经济系担任助理教授与副教授，2008-2020年在新加坡管理大学经济学院先后担任副教授、教授与李光前讲席教授。2020年7月加盟清华大学经济管理学院，为经济系C.V.Starr讲席教授与国家人才计划专家。现为中国数量经济学会副会长。

苏良军教授长期从事理论计量经济领域的研究工作,主要集中在非参数计量经济学、面板数据分析、大数据与机器学习等方向。已经在Econometrica、Econometric Theory、IEEE Transactions on Information Theory、Journal of Applied Econometrics、Journal of Econometrics、Journal of the American Statistical Association、Journal of Business & Economic Statistics、Quantitative Economics等国际一流经济学、统计学与信息学杂志发表论文八十余篇，并编辑出版了两本书。研究结果已被多部世界权威或知名面板数据与非参数计量经济学教科书引用，包括Li和Racine (2007, Nonparametric Econometrics)，与Henderson和Parmeter (2015, Applied Nonparametric Econometrics)，Hsiao (2014, Panel Data Analysis, 3rd edition)，Pesaran (2015, Time Series and Panel Data Econometrics)等。

现在担任计量经济学一流期刊Econometric Theory的联合主编（co-editor），Journal of Econometrics、Journal of Business Economics & Statistics 和 Econometric Reviews的副主编（associate editor)。先后多次获中国与新加坡国家（重点）项目基金资助，2007年获北京大学奖教金，2011年获新加坡李光耀科研奖，2014年获Econometric Theory Multa Scripsit奖并成为Journal of Econometrics会士。2014年后被多次纳入世界名人榜或科学与工程领域的名人录。现为Rimini Centre for Economic Analysis资深会士。