2019年9月26日下午，应广州大学经济与统计学院和岭南统计科学研究中心的邀请，在行政东前座412会议室，西南财经大学常晋源教授作了题为“A Power One Test for Unit Roots Based on Sample Autocovariances”的学术报告——暨“羊城讲坛”第五十九讲，旨在进一步提高年轻学者及研究生对相关研究的理解。此次讲座由张兴发老师主持，相关专业的师生参加了此次讲座。

常晋源教授2013年7月于北京大学光华管理学院取得经济学博士学位，2013年9月至2017年2月在澳大利亚墨尔本大学数学与统计学院任研究员，2017年3月开始全职在西南财经大学统计学院工作。现为西南财经大学数据科学与商业智能联合实验室执行主任、教授、博士生导师、四川省特聘专家、四川省统计专家咨询委员会，是国家青年“QR计划”特聘专家和教育部青年。主要从事“超高维数据分析”和“高频金融数据分析”两个领域的研究。先后以第一作者在《Annals of Statistics》《Biometrika》《Biometrics》和《Journal of Econometrics》等统计学与计量经济学国际顶级学术期刊发表论文十余篇。

现目前正担任统计学国际顶级学术期刊Journal of the Royal Statistical Society Series B、统计学国际知名学术期刊Statistica Sinica以及计量经济学国际著名学术期刊Journal of Business & Economic Statistics的Associate Editor。

报告的主要内容：We propose a new unit-root test for a stationary null hypothesis H_0 against a unit-root alternative H1. Our approach is nonparametric as the hull hypothesis only assumes that the process concerned is I(0) without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function (ACF) converges to the finite population ACF for an I(0) process while it diverges to infinity with probability approaching one for a process with unit-roots. Therefore the new test rejects the null hypothesis for the large values of the sample ACF. To address the technical challenge `how large is large', we split the sample and establish an appropriate normal approximation for the null-distribution of the test statistic. The substantial discriminative power of the new test statistic is rooted from the fact that it takes finite value under H0 and diverges to infinity with probability approaching one under H1. This allows us to truncate the critical values of the test to make it with the asymptotic power one. It also alleviates the loss of power due to the sample-splitting. The finite sample properties of the test are illustrated by simulation which shows its stable and more powerful performance in comparison with the KPSS test .