Empirical Likelihood for Regression Discontinuity Design

Taisuke Otsu and Ke-Li Xu ()
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Taisuke Otsu: Cowles Foundation, Yale University, https://cowles.yale.edu/

No 1799, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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. Bandwidth selection methods, higher-order properties, and extensions to incorporate additional covariates and parametric functional forms are also discussed.

Keywords: Empirical likelihood; Nonparametric methods; Regression discontinuity design; Treatment effect (search for similar items in EconPapers)
JEL-codes: C12 C14 C21 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2011-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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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 (2014)
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