報告題目：Testing for unit roots based on sample autocovariances
報 告 人：常晉源 教授 西南财經大學
報告時間：2020年11月20日 8:30-9:30
報告地點： 騰訊會議413548523 密碼123456
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：
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.
報告人簡介：
常晉源，西南财經大學數據科學與商業智能聯合實驗室執行主任、教授、博士生導師，主要從事超高維數據分析和高頻金融數據分析兩個領域的研究。已在統計學與計量經濟學國際頂級學術期刊Annals of Statistics、Biometrika、Journal of Econometrics、Journal of the American Statistical Association等上發表論文10餘篇。現擔任Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics和Statistica Sinica的Associate Editor以及《應用概率統計》的編委。
