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Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs

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  • Claudia Noack
  • Christoph Rothe

Abstract

We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson–Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with “delta method” approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, they are also valid under a wide range of other empirically relevant conditions, such as setups with discrete running variables, donut designs, and weak identification.

Suggested Citation

  • Claudia Noack & Christoph Rothe, 2024. "Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs," Econometrica, Econometric Society, vol. 92(3), pages 687-711, May.
  • Handle: RePEc:wly:emetrp:v:92:y:2024:i:3:p:687-711
    DOI: 10.3982/ECTA19466
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    References listed on IDEAS

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    Cited by:

    1. Timothy B. Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," CeMMAP working papers 28/24, Institute for Fiscal Studies.

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