Local Polynomial Order in Regression Discontinuity Designs

时间:2021-01-04         阅读:

光华讲坛——社会名流与企业家论坛第5621期

主题Local Polynomial Order in Regression Discontinuity Designs

主讲人康奈尔大学 裴撰

主持人工商管理学院 刘忠教授

时间2021年1月7日(周四)9:30-11:00

直播平台及会议ID腾讯会议会议ID:993 828 832

主办单位:工商管理学院 科研处

主讲人简介:

Zhuan Pei joined the Department of Policy Analysis and Management at Cornell University in July 2015 as an assistant professor. His fields of interest including: Labor Economics, Applied Micro-econometrics and Public Policy. Zhuan Pei has already publishedseveral articles in the journal of AER、Econometrica、Advances in Econometrics and so on. In his research, he investigates the effect and design of social and employment programs and studies applied micro-econometric methods in causal inference. Prior to Cornell, he was a postdoctoral economist at the W. E. Upjohn Institute for Employment Research from 2012 to 2013 and an assistant professor of economics at Brandeis University between 2013 and 2015.

裴撰于2015年作为助理教授加入康奈尔大学政策分析和管理系。他的主要兴趣:劳动力经济学、应用微观计量和公共政策评估。Zhuan Pei教授的文章发表在AER、Econometrica、Advances in Econometrics等杂志上。在此之前,他曾在 W. E. Upjohn Institute for Employment Research进行博士后研究(2012至2013年),和担任 Brandeis大学经济系副教授(2013至2015年)


内容提要:

Treatment effect estimates in regression discontinuity (RD) designs are often sensitive to the choice of bandwidth and polynomial order, the two important ingredients of widely used local regression methods. While Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) provide guidance on bandwidth, the sensitivity to polynomial order still poses a conundrum to RD practitioners. It is understood in the econometric literature that applying the argument of bias reduction does not help resolve this conundrum, since it would always lead to preferring higher orders. We therefore extend the frameworks of Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) and use the asymptotic mean squared error of the local regression RD estimator as the criterion to guide polynomial order selection. We show in Monte Carlo simulations that the proposed order selection procedure performs well, particularly in large sample sizes typically found in empirical RD applications. This procedure extends easily to fuzzy regression discontinuity and regression kink designs.

回归断点设计对带宽和多项式的阶数敏感。过去文献表明,利用减少偏差的参数并不能解决此问题。本文推广了Imbens and Kalyanaraman (2012)和 Calonico, Cattaneo and Titiunik (2014)两文的框架,使用局域断点估计器的均方差作为选择阶数的标准。模拟显示,本文提出的方法,比在实证分析中常用的方法效能更好,尤其在大样本中。本文的方法可容易推广到模糊断点和弯折研究设计中。