Robust Quantile Factor Models

摘要：

In factor model analysis strong factors are commonly assumed. There has some progress made in the literature in allowing for weaker factors/loadings, in which case some specific structure is imposed on the strength of the factors/loadings. In this paper we consider quantile regression of factor models with possibly very weak factors, without imposing a similar structure. We establish large sample properties of our estimator, which performs well in finite samples.

报告时间：2024年1月10日10:00

线下地点：威廉希尔6-210会议室

主办单位：william hill中文网

         英国威廉希尔公司数量经济研究中心

嘉宾简介：

陈松年，浙江大学教授，著名经济学家，世界计量经济学会院士，是浙江大学青山商学高等研究院引进的首位浙大青山讲席教授，主要从事理论与应用微观计量学研究工作。陈教授曾任新加坡国立大学经济学系讲席教授、香港科技大学经济系讲席教授，是计量经济学领域享有盛誉的国际知名学者，特别在截断删失回归、分位数回归、样本选择模型等领域的研究享誉国际。长期担任计量经济学顶刊Journal of Econometrics的副主编，是该期刊的荣誉会员，已在Econometrica, Review of Economic Studies, Journal of Econometrics, Econometric Theory, Annals of Statistics, Journal of American Statistical Association, Journal of Business and Economic Statistics等国际学术期刊发表论文四十余篇。个人主页：https://person.zju.edu.cn/snchen。