讲座题目：Factor and idiosyncratic empirical processes
主讲人：孔新兵
时间：3月30日（周五）下午14:30
地点：燕山校区一号教学楼统计学院资料室
报告摘要: The distributions of the factor return and specific error for an individual variable are important in forecasting and applications. However, they are not identified with low dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the factor return and specific error can be estimated consistently. Based on the estimated factor returns and residual errors, we construct the empirical processes for estimation of the distribution functions of the factor return and specific error, respectively. We prove that the two empirical processes are oracle efficient when $T=o(p)$ where $p$ and $T$ are the dimension and sample size, respectively. This demonstrates that the factor and residual empirical processes behave as well as the empirical processes pretending that the factor returns and specific errors for an individual variable are directly observable. Based on this oracle property, we construct simultaneous confidence bands (SCBs) for the distributions of the factor return and specific error. For the first order consistency of the estimated factor and residual distributions, $\sqrt{T}=o(p)$ suffices. Extensive simulation studies check that the estimated bands have good coverage frequencies. Our real data analysis shows that the factor return distribution has a structural change during the crisis in 2008, while the idiosyncratic return distribution does not change much.
主讲人简介：南京审计大学、统计与数学学院/统计与大数据研究院、教授。2011年7月-2014年7月，复旦大学管理学院助理教授及副教授。2014年8月-2017年2月，苏州大学数学学院及高等统计与计量经济研究中心教授。孔新兵教授博士毕业于香港科技大学，目前的研究领域为经济统计、数理统计、网络数据统计，在统计学顶级期刊Annals of Statistics, Journal of the American Statistical Association, Biometrika以及计量经济学顶级期刊Journal of Econometric, Journal of Business and Economic Statistics发表多篇研究论文。孔新兵教授目前为国际统计协会(ISI)会员，现场统计研究会高维统计分会理事，江苏省“双创博士”，曾获香港数学会“最佳博士论文奖”，复旦大学管理学院年度“青年新星奖”等，主持多项国家自然科学基金以及教育部人文社科基金。
科研处、统计学院（大数据与指数研究院）
山东省大数据研究会
2018年3月25日
