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Inference in Regression Discontinuity Designs with a Discrete Running Variable

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  • Kolesár, Michal

    (Princeton University)

  • Rothe, Christoph

    (University of Mannheim)

Abstract

We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable. We derive theoretical results and present simulation and empirical evidence showing that these CIs have poor coverage properties and therefore recommend that they not be used in practice. We also suggest alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function.

Suggested Citation

  • Kolesár, Michal & Rothe, Christoph, 2016. "Inference in Regression Discontinuity Designs with a Discrete Running Variable," IZA Discussion Papers 9990, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp9990
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    References listed on IDEAS

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    More about this item

    Keywords

    discrete running variable; regression discontinuity design; clustered standard errors;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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