CFDP 1799

Empirical Likelihood for Regression Discontinuity Design


Publication Date: May 2011

Pages: 36


This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity designs and treat the regression functions as nonparametric. The proposed inference procedures do not require asymptotic variance estimation and the confidence sets have natural shapes, unlike the conventional Wald-type method. These features are illustrated by simulations and an empirical example which evaluates the effect of class size on pupils’ scholastic achievements. Bandwidth selection methods, higher-order properties, and extensions to incorporate additional covariates and parametric functional forms are also discussed.


Empirical likelihood, Nonparametric methods, Regression discontinuity design, Treatment effect

JEL Classification Codes:  C12, C14, C21


Published in Journal of Econometrics (May 2015), 186(1): 94-112 [DOI]