| 主讲人 |
冷旋 |
简介 |
<p>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.</p> |
| 时间 |
2025-06-03 (Tuesday) 16:30-18:00 |
地点 |
厦大经济楼C108（线下分会场）、中国科学院数学与系统科学研究院南楼N204、腾讯会议 ID：651 585 346 |
| 讲座语言 |
中文 |
主办单位 |
厦门大学邹至庄经济研究院、厦门大学-中国科学院计量建模与经济政策研究基础科学中心、中国科学院数学与系统科学研究院预测科学研究中心、中国科学院大学经济与管理学院 |
| 承办单位 |
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类型 |
系列讲座 |
| 联系人信息 |
许老师，电话：2182991，邮箱：ysxu@xmu.edu.cn |
主持人 |
洪永淼 |
| 专题网站 |
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专题 |
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| 主讲人简介 |
<p>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。</p> |
期数 |
“邹至庄讲座”青年学者论坛（第81期） |
