首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs
Authors:Andrew Gelman  Guido Imbens
Institution:1. Department of Statistics and Department of Political Science, Columbia University, New York, NY, 10027 (gelman@stat.columbia.edu);2. Graduate School of Business, Stanford University, Stanford, CA 94305;3. NBER, Stanford University, Stanford, CA 94305 (imbens@stanford.edu)
Abstract:It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.
Keywords:Causal identification  Policy analysis  Polynomial regression  Regression discontinuity  Uncertainty  
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号