报告题目：Kolmogorov-Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach
内容摘要：A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit root and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to the aforementioned irregularities. We develop an adjusted-range based Kolmogorov-Smirnov type test for structural breaks in mean for both univariate and multivariate time series and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the "better size but less power" phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.
主讲人简介：孙佳婧，中国科学院大学经济与管理学院副教授，特许金融分析师，主要研究领域包括金融学、计量经济学、统计学等。曾在Journal of Time Series Analysis、Journal of Multivariate Analysis、Energy Economics、Economics Letters以及《应用概率统计》、《统计研究》上发表多篇论文。
报告时间：2023年4月18日（周二），16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：479 3348 6244
