Empirical likelihood for regression discontinuity design

Taisuke Otsu, Yukitoshi Matsushita and Ke-Li Xu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: J1 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2014-02
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http://eprints.lse.ac.uk/58065/ Open access version. (application/pdf)

Related works:
Journal Article: Empirical likelihood for regression discontinuity design (2015)
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 (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:58065

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