报告题目：Testing for unit roots based on sample autocovariances

报 告 人：常晋源 教授 西南财经大学

报告时间：2020年11月20日 8:30-9:30

报告地点： 腾讯会议413548523 密码123456

校内联系人：朱复康  fzhu@jlu.edu.cn

报告摘要：

     We propose a new unit-root test for a stationary null hypothesis H0 against a unit-root alternative H1. Our approach is nonparametric as H0 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 for a process with unit-roots. Therefore the new test rejects H0 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 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 (Kwiatkowski et al., 1992). The test is implemented in a user-friendly R-function.

报告人简介：

    常晋源，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，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。已在统计学与计量经济学国际顶级学术期刊Annals of Statistics、Biometrika、Journal of Econometrics、Journal of the American Statistical Association等上发表论文10余篇。现担任Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics和Statistica Sinica的Associate Editor以及《应用概率统计》的编委。