Report title: Testing for unit roots based on sample autocovariances

Reporter: Professor Chang Jinyuan Southwestern University of Finance and Economics

Reporting time: 8:30-9:30, November 20, 2020

Report location: Tencent meeting 413548523 password 123456

School contact: Zhu Fukang fzhu@jlu.edu.cn

Report summary:

    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-splitti ng. 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.

Brief introduction of the speaker:

   Chang Jinyuan, Executive Director, Professor, and Doctoral Supervisor of the Data Science and Business Intelligence Joint Laboratory of Southwestern University of Finance and Economics, is mainly engaged in research in the two fields of ultra-high-dimensional data analysis and high-frequency financial data analysis. He has published more than 10 papers in the top international academic journals Annals of Statistics, Biometrika, Journal of Econometrics, Journal of the American Statistical Association, etc. Currently serving as the Associate Editor of Journal of the Royal Statistical Society Series B, Journal of Business & Economic Statistics and Statistics Sinica and the editorial board member of Applied Probability and Statistics.