报告题目：Reinforced Tail Quantile Regression
内容摘要：Standard quantile regression suffers from high variability in the tail regions, particularly for heavy-tailed data. As the quantile level approaches 1 or 0, the estimator exhibits non-standard asymptotic behavior, posing challenges for statistical inference. In this paper, we propose a novel tail-reinforced quantile regression estimator that substantially reduces estimation variance by leveraging the power-law behavior inherent in heavy-tailed distributions. Our estimator is both consistent and asymptotically normal. To facilitate inference, we further introduce a sequential multiplier bootstrap procedure using multiple sets of random weights. Simulation studies demonstrate that our method yields notably narrower confidence intervals compared to standard quantile regression, while achieving near-exact coverage through the bootstrap procedure. We apply the proposed method to assess the marginal effect of education on upper income percentiles using a unique dataset from the Chinese Twins Survey. The results reveal a significantly positive effect of education in the upper tail, in contrast to existing approaches, which often yield insignificant effects accompanied by wide confidence intervals.
主讲人简介：冷旋，现为厦门大学经济学院与王亚南经济研究院副教授，博士毕业于中国科学技术大学统计学专业。研究方向包括面板分位数回归、极值统计、风险管理与强化学习等。多篇研究成果发表于Journal of Econometrics、Insurance: Mathematics and Economics、Extremes、Journal of Financial Econometrics 等国际期刊。
报告时间：2025年6月3日(周二)，16:30-18:00
线下地点：厦门大学经济楼C108
线上地点：腾讯会议 ID：651 585 346
