报告题目：Improving Extremal Quantile Regression
内容摘要：Extreme-order regression quantiles suffer from inconsistency and a non-normal asymptotic distribution. We propose a novel extremal quantile regression estimator based on extreme value theory to improve the estimation of moment conditions. Our estimator is both consistent and asymptotically normal. Furthermore, we establish the asymptotic validity of bootstrap inference using random weights. Simulation results show that our estimator achieves smaller estimation errors and that our bootstrap confidence intervals exhibit good coverage. Finally, we apply our method to analyze the extremely low percentiles of live infant birth weight. This work is in collaboration with Yongmiao Hong, Xuan Leng, and Yizhou Zhang.
主讲人简介：Yi He is an Associate Professor in the Quantitative Economic Section at the University of Amsterdam. He earned his master’s degree from the University of Cambridge and his PhD from Tilburg University in 2016. Prior to returning to the Netherlands, he served as a tenured Assistant Professor in the Department of Econometrics and Business Statistics at Monash University in Australia. His research focuses on high-dimensional econometrics, random matrix theory, extreme value statistics, bootstrapping, and machine learning. His work has been featured in prestigious journals, including the Journal of the American Statistical Association, The Annals of Statistics, Journal of the Royal Statistical Society - Series B, Journal of Business & Economic Statistics, and Journal of Econometrics. Yi's breakthroughs in extreme value statistics have earned him a nomination for the 2025 Van Dantzig Award in Statistics and Operations Research in the Netherlands. His current research explores dense time series models with complex network interactions in high-dimensional econometrics.
报告时间：2025年2月25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：730 161 211
