Empirical likelihood for regression discontinuity design

Taisuke Otsu, Ke-Li Xu and Yukitoshi Matsushita

Journal of Econometrics, 2015, vol. 186, issue 1, 94-112

Abstract: 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.

Keywords: Empirical likelihood; Nonparametric methods; Regression discontinuity design; Treatment effect; Bartlett correction (search for similar items in EconPapers)
JEL-codes: C12 C14 C21 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Related works:
Working Paper: Empirical likelihood for regression discontinuity design (2015)
Working Paper: Empirical Likelihood for Regression Discontinuity Design (2014)
Working Paper: Empirical likelihood for regression discontinuity design (2014)
Working Paper: Empirical Likelihood for Regression Discontinuity Design (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:186:y:2015:i:1:p:94-112

DOI: 10.1016/j.jeconom.2014.04.023

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