arXiv 30 Jun 2020 · Econometrics · 3 citations (OpenAlex)
arXiv:2006.16997 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of inference in Difference-in-Differences (DID) when there are few treated units and errors are spatially correlated. We first show that, when there is a single treated unit, some existing inference methods designed for settings with few treated and many control units remain asymptotically valid when errors are weakly dependent. However, these methods may be invalid with more than one treated unit. We propose alternatives that are asymptotically valid in this setting, even when the relevant distance metric across units is unavailable.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sommers, B. D., Long, S. K., and Baicker, K (2014) Changes in mortality after Massachusetts health care reform | 0.928 | 10 | 4 | 80% |
| 2 | Benjamini, Y. and Hochberg, Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing | 0.874 | 6 | 4 | 67% |
| 3 | Ferman, B (2023) Inference in difference-in-differences: How much should we trust in independent clusters? self | 0.737 | 3 | 3 | 67% |
| 4 | Conley, T. G. and Taber, C. R (2011) Inference with Difference in Differences with a Small Number of Policy Changes | 0.737 | 3 | 2 | 100% |
| 5 | Ferman, B. and Pinto, C (2019) Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity self | 0.737 | 3 | 2 | 100% |
| 6 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An exact and robust conformal inference method for counterfactual and synthetic controls | 0.644 | 2 | 2 | 100% |
| 7 | Canay, I. A., Romano, J. P., and Shaikh, A. M (2017) Randomization tests under an approximate symmetry assumption | 0.644 | 2 | 2 | 100% |
| 8 | MacKinnon, J. G. and Webb, M. D (2020) Randomization inference for difference-in-differences with few treated clusters | 0.644 | 2 | 2 | 100% |
| 9 | Alvarez, L. and Ferman, B (2023) Extensions for inference in difference-in-differences with few treated clusters self | 0.644 | 2 | 2 | 100% |
| 10 | de Chaisemartin, C. and D'Haultfoeuille, X (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.511 | 3 | 2 | 33% |
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