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Regression Discontinuity Design with Continuous Measurement Error in the Running Variable

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  • Davezies, Laurent

    () (CREST)

  • Le Barbanchon, Thomas

    () (Bocconi University)

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 non-differential 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

  • Davezies, Laurent & Le Barbanchon, Thomas, 2017. "Regression Discontinuity Design with Continuous Measurement Error in the Running Variable," IZA Discussion Papers 10801, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp10801
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    Cited by:

    1. Davezies, Laurent & Le Barbanchon, Thomas, 2017. "Regression discontinuity design with continuous measurement error in the running variable," Journal of Econometrics, Elsevier, vol. 200(2), pages 260-281.
    2. YANAGI, Takahide, 2015. "Regression Discontinuity Designs with Nonclassical Measurement Error," Discussion Papers 2015-09, Graduate School of Economics, Hitotsubashi University.
    3. Le Barbanchon, Thomas, 2016. "The effect of the potential duration of unemployment benefits on unemployment exits to work and match quality in France," Labour Economics, Elsevier, vol. 42(C), pages 16-29.

    More about this item

    Keywords

    regression discontinuity design; measurement error;

    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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