报告题目:Max-Sum tests for cross-sectional dependence of high-dimensional panel data
主讲人:冯龙
报告摘要: We consider a testing problem for cross-sectional dependence for high-dimensional panel data, where the number of cross- -sectional units is potentially much larger than the number of observations. The cross-sectional dependence is described through a linear regression model. We study three tests named the sum test, the max test and the max sum test, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for sparse and non-sparse residuals in the linear regressions, respectively. And the max -sum test is devised to com-promise both situations on the residuals. Indeed, our simulation shows that the max- -sum test outperforms the previous two tests. This makes the max sum test very useful in practice where sparsity or not for a set of data is usually vague. Towards the theoretical analysis of the three tests, we have settled two conjectures regarding the sum of squares of sample correlation coefficients asked by Pesaran (2004 and 2008). In addition, we establish the asymptotic theory for maxima of sample correlations coefficients appeared in the linear regression model for panel data, which is also the first successful attempt to our knowledge. To study the max-sum test, we create a novel method to show asymptotic independence between maxima and sums of dependent random variables. We expect the method itself is useful for other problems of this nature. Finally, an extensive simulation study as well as a case study are carried out. They demonstrate advantages of our proposed methods in terms of both empirical powers and robustness for residuals regardless of sparsity or not.
冯龙简介:东北师范大学数学与统计学院副教授。博士毕业于南开大学。在国际高水平杂志JASA, AOS, Biometrika等发表SCI论文20篇。 主持一项国家自然科学基金青年项目.
报告时间: 2020年11月23日下午3:30-6:00
报告地点:南湖校区教学科研楼405室
主办单位:科研处/数学与统计学院
