Econometrica

Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: May, 2024, Volume 92, Issue 3

Bias-Aware Inference in Fuzzy Regression Discontinuity Designs

https://doi.org/10.3982/ECTA19466
p. 687-711

Claudia Noack, Christoph Rothe

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.


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Supplemental Material

Supplement to "Bias-Aware Inference in Fuzzy Regression Discontinuity Designs"

Claudia Noack and Christoph Rothe

This supplemental appendix contains material not found within the manuscript.

Supplement to "Bias-Aware Inference in Fuzzy Regression Discontinuity Designs"

Claudia Noack and Christoph Rothe

The replication package for this paper is available at https://doi.org/10.5281/zenodo.10724847. The Journal checked the data and codes included in the package for their ability to reproduce the results in the paper and approved online appendices. Given the highly demanding nature of the algorithms, the reproducibility checks were run on a simplified version of the code, which is also available in the replication package.