Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
arXiv 1 Sep 2018 · Econometrics · publishedThe Stata Journal Promoting communications on statistics and Stata (2014) · 716 citations (OpenAlex)
arXiv:1809.00236 · PDF · DOI · OpenAlex · Extracted main text
Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smallest coverage error. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in R and Stata packages.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik (2019) Regression discontinuity designs using covariates self | 1.000 | 7 | 4 | 100% |
| 2 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs self | 1.000 | 6 | 3 | 100% |
| 3 | Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik (2017) rdrobust: Software for regression discontinuity designs self | 0.843 | 3 | 3 | 100% |
| 4 | Cattaneo, M. D., R. Titiunik, and G. Vazquez-Bare (2017) Comparing inference approaches for rd designs: A reexamination of the effect of head start on child mortality self | 0.737 | 3 | 2 | 100% |
| 5 | Cattaneo, M. D. and G. Vazquez-Bare (2016) The choice of neighborhood in regression discontinuity designs self | 0.737 | 3 | 2 | 100% |
| 6 | Cheng, M.-Y., J. Fan, and J. S. Marron (1997) On automatic boundary corrections | 0.737 | 3 | 2 | 100% |
| 7 | Arai, Y. and H. Ichimura (2016) Optimal bandwidth selection for the fuzzy regression discontinuity estimator | 0.644 | 2 | 2 | 100% |
| 8 | Arai, Y. and H. Ichimura (2018) Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator | 0.644 | 2 | 2 | 100% |
| 9 | Bartalotti, O. and Q. Brummet (2017) Regression discontinuity designs with clustered data | 0.644 | 2 | 2 | 100% |
| 10 | Cattaneo, M. D., L. Keele, R. Titiunik, and G. Vazquez-Bare (2016) Interpreting regression discontinuity designs with multiple cutoffs self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.