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Inference in Regression Discontinuity Designs with a Discrete Running Variable


  • Kolesár, Michal

    () (Princeton University)

  • Rothe, Christoph

    () (University of Mannheim)


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

    1. Paul J. Devereux & Robert A. Hart, 2010. "Forced to be Rich? Returns to Compulsory Schooling in Britain," Economic Journal, Royal Economic Society, vol. 120(549), pages 1345-1364, December.
    2. Yingying Dong, 2012. "Regression Discontinuity Applications with Rounding Errors in the Running Variable," Working Papers 111206, University of California-Irvine, Department of Economics.
    3. Andrew Gelman & Guido Imbens, 2014. "Why High-order Polynomials Should not be Used in Regression Discontinuity Designs," NBER Working Papers 20405, National Bureau of Economic Research, Inc.
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    15. Rebecca Mary Myerson & Reginald Tucker-Seeley & Dana Goldman & Darius N. Lakdawalla, 2019. "Does Medicare Coverage Improve Cancer Detection and Mortality Outcomes?," NBER Working Papers 26292, National Bureau of Economic Research, Inc.
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    More about this item


    regression discontinuity design; discrete running variable; clustered standard errors;

    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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