Claudia Noack, Christoph Rothe
arXiv 11 Jun 2019 · Econometrics · publishedEconometrica (2024) · 20 citations (OpenAlex)
arXiv:1906.04631 · PDF · DOI · OpenAlex · Extracted main text
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, due to their particular construction they are also valid under a wide range of empirically relevant conditions in which existing methods can fail, such as setups with discrete running variables, donut designs, and weak identification.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Imbens, G. and S. Wager (2019) Optimized regression discontinuity designs | 1.000 | 5 | 4 | 100% |
| 2 | Armstrong, T. and M. Kolesár (2018) Optimal inference in a class of regression models | 1.000 | 5 | 3 | 100% |
| 3 | Armstrong, T. and M. Kolesár (2020) Simple and honest confidence intervals in nonparametric regression | 0.899 | 11 | 6 | 73% |
| 4 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.843 | 3 | 3 | 100% |
| 5 | Hahn, J., P. Todd, and W. Van der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 0.843 | 3 | 3 | 100% |
| 6 | Kolesár, M. and C. Rothe (2018) Inference in Regression Discontinuity Designs with a Discrete Running Variable | 0.843 | 3 | 3 | 100% |
| 7 | Feir, D., T. Lemieux, and V. Marmer (2016) Weak identification in fuzzy regression discontinuity designs | 0.737 | 3 | 2 | 100% |
| 8 | Battistin, E., A. Brugiavini, E. Rettore, and G. Weber (2009) The retirement consumption puzzle: evidence from a regression discontinuity approach | 0.644 | 4 | 1 | 100% |
| 9 | Li, K.-C (1989) Honest confidence regions for nonparametric regression | 0.644 | 2 | 2 | 100% |
| 10 | Abadie, A. and G. W. Imbens (2006) Large Sample Properties of Matching Estimators for Average Treatment Effects | 0.405 | 1 | 1 | 100% |
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