arXiv 5 Jul 2024 · Econometrics · 10 citations (OpenAlex)
arXiv:2407.09565 · PDF · DOI · OpenAlex · Extracted main text
I propose an event study extension of Synthetic Difference-in-Differences (SDID) estimators. I show that, in simple and staggered adoption designs, estimators from Arkhangelsky et al. (2021) can be disaggregated into dynamic treatment effect estimators, comparing the lagged outcome differentials of treated and synthetic controls to their pre-treatment average. Estimators presented in this note can be computed using the sdid_event Stata package.
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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 | Damian Clarke, Daniel Pailanir, Susan Athey, and Guido Imbens (2023) Synthetic difference in differences estimation | 0.693 | 5 | 1 | 100% |
| 2 | Dmitry Arkhangelsky, Susan Athey, David Hirshberg, Guido Imbens, and… (2021) Synthetic difference-in-differences | 0.511 | 2 | 1 | 100% |
| 3 | Kirill Borusyak, Xavier Jaravel, and Jann Spiess (2024) Revisiting event-study designs: robust and efficient estimation | 0.405 | 1 | 1 | 100% |
| 4 | Clément de Chaisemartin and Xavier D’Haultfoeuille (2023) Difference-in-differences for simple and complex natural experiments | 0.405 | 1 | 1 | 100% |
| 5 | John Gardner (2022) Two-stage differences in differences | 0.405 | 1 | 1 | 100% |
| 6 | Licheng Liu, Ye Wang, and Yiqing Xu (2024) A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data | 0.405 | 1 | 1 | 100% |
Showing the top 6 of 6 scored citations.