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    1. Local Polynomial Order in Regression Discontinuity Designs

      時間:2021-01-04         閱讀:


      主題Local Polynomial Order in Regression Discontinuity Designs

      主講人康奈爾大學 裴撰

      主持人工商管理學院 劉忠教授


      直播平臺及會議ID騰訊會議會議ID:993 828 832

      主辦單位:工商管理學院 科研處


      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年作為助理教授加入康奈爾大學政策分析和管理系。他的主要興趣:勞動力經濟學、應用微觀計量和公共政策評估。Zhuan Pei教授的文章發表在AER、Econometrica、Advances in Econometrics等雜志上。在此之前,他曾在 W. E. Upjohn Institute for Employment Research進行博士后研究(2012至2013年),和擔任 Brandeis大學經濟系副教授(2013至2015年)


      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.

      回歸斷點設計對帶寬和多項式的階數敏感。過去文獻表明,利用減少偏差的參數并不能解決此問題。本文推廣了Imbens and Kalyanaraman (2012)和 Calonico, Cattaneo and Titiunik (2014)兩文的框架,使用局域斷點估計器的均方差作為選擇階數的標準。模擬顯示,本文提出的方法,比在實證分析中常用的方法效能更好,尤其在大樣本中。本文的方法可容易推廣到模糊斷點和彎折研究設計中。