2019
DOI: 10.1920/wp.cem.2019.6719
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Honest inference for discrete outcomes

Abstract: We investigate the consequences of discreteness in the assignment variable in regressiondiscontinuity designs for cases where the outcome variable is itself discrete. We nd that constructing condence intervals that have the correct level of coverage in these cases is sensitive to the assumed distribution of unobserved heterogeneity. Since local linear estimators are improperly centered, a smaller variance for unobserved heterogeneity in discrete outcomes actually requires larger condence intervals, since stand… Show more

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