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Regression Discontinuity Design with Continuous Measurement Error in the Running Variable

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  • Le Barbanchon, Thomas
  • Davezies, Laurent

Abstract

Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming nondifferential measurement error, we propose a consistent nonparametric estimator of the LATE when the discrepancy between the true running variable and its noisy measure is observed in an auxiliary sample of treated individuals, and when there are treated individuals at any value of the true running variable - two-sided fuzzy designs. We apply our method to estimate the effect of receiving unemployment benefits.

Suggested Citation

  • Le Barbanchon, Thomas & Davezies, Laurent, 2017. "Regression Discontinuity Design with Continuous Measurement Error in the Running Variable," CEPR Discussion Papers 11775, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:11775
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    More about this item

    Keywords

    Regression discontinuity design; Measurement error; Unemployment insurance;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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