arXiv 28 Nov 2020 · Econometrics · 2 citations (OpenAlex)
arXiv:2011.14216 · PDF · DOI · OpenAlex · Extracted main text
We provide an inference procedure for the sharp regression discontinuity design (RDD) under monotonicity, with possibly multiple running variables. Specifically, we consider the case where the true regression function is monotone with respect to (all or some of) the running variables and assumed to lie in a Lipschitz smoothness class. Such a monotonicity condition is natural in many empirical contexts, and the Lipschitz constant has an intuitive interpretation. We propose a minimax two-sided confidence interval (CI) and an adaptive one-sided CI. For the two-sided CI, the researcher is required to choose a Lipschitz constant where she believes the true regression function to lie in. This is the only tuning parameter, and the resulting CI has uniform coverage and obtains the minimax optimal length. The one-sided CI can be constructed to maintain coverage over all monotone functions, providing maximum credibility in terms of the choice of the Lipschitz constant. Moreover, the monotonicity makes it possible for the (excess) length of the CI to adapt to the true Lipschitz constant of the unknown regression function. Overall, the proposed procedures make it easy to see under what conditions on the underlying regression function the given estimates are significant, which can add more transparency to research using RDD methods.
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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 | Armstrong, T. B. and M. Kolesár (2020) b): Simple and honest confidence intervals in nonparametric regression | 1.000 | 5 | 5 | 100% |
| 2 | Imbens, G. and S. Wager (2019) Optimized regression discontinuity designs | 0.961 | 9 | 4 | 89% |
| 3 | Babii, A. and R. Kumar (2020) Isotonic regression discontinuity designs | 0.950 | 7 | 5 | 86% |
| 4 | Donoho, D. L (1994) Statistical Estimation and Optimal Recovery | 0.941 | 6 | 3 | 83% |
| 5 | Armstrong, T. B. and M. Kolesár (2018) a): Optimal inference in a class of regression models | 0.937 | 17 | 6 | 82% |
| 6 | Lee, D. S (2008) Randomized experiments from non-random selection in US House elections | 0.928 | 5 | 4 | 80% |
| 7 | Armstrong, T. B. and M. Kolesár (2018) b): A simple adjustment for bandwidth snooping | 0.811 | 4 | 2 | 100% |
| 8 | Cai, T. T. and M. G. Low (2004) An adaptation theory for nonparametric confidence intervals | 0.811 | 4 | 2 | 100% |
| 9 | Kwon, K. and S. Kwon (2020) Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity, Working paper self | 0.754 | 7 | 4 | 43% |
| 10 | Armstrong, T. B. and M. Kolesár (2018) c): Supplement to 'Optimal inference in a class of regression models' | 0.737 | 3 | 3 | 67% |
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