Empirical likelihood for regression discontinuity design

Taisuke Otsu, Ke Li Xu, Yukitoshi Matsushita

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)94-112
Number of pages19
JournalJournal of Econometrics
Volume186
Issue number1
DOIs
Publication statusPublished - 2015 May 1
Externally publishedYes

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Keywords

  • Bartlett correction
  • Empirical likelihood
  • Nonparametric methods
  • Regression discontinuity design
  • Treatment effect

ASJC Scopus subject areas

  • Economics and Econometrics

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