讲座主题：Quantile Regression with Group-level Treatments

主讲嘉宾：陈松年，浙江大学教授、世界计量经济学会院士

讲座时间：2023年5月26日（周五） 10:00-12:00

讲座地点：3044am永利集团3044noc沙河校区学院楼11号楼308

嘉宾简介：陈松年，著名经济学家、世界计量经济学会院士、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。陈教授1986年毕业于复旦大学数学系，1994年获普林斯顿大学经济学博士学位。研究领域为理论与应用微观计量学。他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。陈教授在计量经济学领域享有盛誉，特别是在微观计量经济学领域具有突出成绩，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。他已在Econometrica, Review of Economic Studies, Journal of Econometrics等国际学术期刊发表论文四十余篇，并在计量经济学顶刊Journal of Econometrics长期担任副主编，是该期刊的荣誉会员。

内容摘要：To study the distributional effects of group level treatments, Angrist and Lang (2004) applied quantile regression with group level regressors, and Chetverikov et al. (2016) proposed a grouped instrumental variables quantile regression estimator, a quantile extension of the Hausman and Taylor’s (1981) instrumental variables estimator for panel data. However, the analyses of distributional effects of group level treatments in Angrist and Lang (2004) and Chetverikov et al. (2016) are incomplete and their models are quite restrictive, and they only allow for heterogenous distributional effects of group-level treatments that corresponds to individual-level unobserved characteristics, but not group-level unobserved characteristics. In other words, Angrist and Lang (2004) and Chetverikov et al. (2016) allow for within group hetergeneous distributional treatment effects, but not between group heterogeneous distributional treatment effects. In this article, we provide a comprehensive analysis by proposing a quantile regression model that allows for heterogenous distributional effects of group level treatments associated with both individual level and group level unobserved characteristics, corresponding to within-group and between-group distributional effects. We propose two step quantile regression and instrumental variables quantile regression estimators, depending on whether the group level treatments are correlated with the group level unobserved characteristics. Large sample properties are presented and simulation results indicate our estimators perform well in finite samples.