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Minimum distance estimator for sharp regression discontinuity with multiple running variables

Author

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  • Choi, Jin-young
  • Lee, Myoung-jae
Abstract
In typical regression discontinuity, a running variable (or ‘score’) crosses a cutoff to determine a treatment. There are, however, many regression discontinuity cases where multiple scores have to cross all of their cutoffs to get treated. One approach to deal with these cases is one-dimensional localization using a single score on the subpopulation with all the other scores already crossing the cutoffs (“conditional one-dimensional localization approach, CON”), which is, however, inconsistent when partial effects are present which occur when some, but not all, scores cross their cutoffs. Another approach is multi-dimensional localization explicitly allowing for partial effects, which is, however, less efficient than CON due to more localizations than in CON. We propose a minimum distance estimator that is at least as efficient as CON, yet consistent even when partial effects are present. A simulation study demonstrates these characteristics of the minimum distance estimator.

Suggested Citation

  • Choi, Jin-young & Lee, Myoung-jae, 2018. "Minimum distance estimator for sharp regression discontinuity with multiple running variables," Economics Letters, Elsevier, vol. 162(C), pages 10-14.
    • Handle: RePEc:eee:ecolet:v:162:y:2018:i:c:p:10-14
    DOI: 10.1016/j.econlet.2017.10.002
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    Regression discontinuity; Multiple running variables; Minimum distance estimator;
    All these keywords.

    JEL classification:

    • 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

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