Publication Date: May 2011
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]