Journal
JOURNAL OF ECONOMETRICS
Volume 186, Issue 1, Pages 94-112Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2014.04.023
Keywords
Empirical likelihood; Nonparametric methods; Regression discontinuity design; Treatment effect; Bartlett correction
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Funding
- National Science Foundation [SES-0720961]
- University of Alberta School of Business through the Canadian utilities faculty award
- Grants-in-Aid for Scientific Research [24730191, 26780133] Funding Source: KAKEN
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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. Furthermore, for the sharp regression discontinuity design, we show that the empirical likelihood statistic admits a higher-order refinement, so-called the Bartlett correction. Bandwidth selection methods are also discussed. (C) 2014 Elsevier B.V. All rights reserved.
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