arXiv 30 Sep 2024 · Econometrics
arXiv:2409.20199 · PDF · DOI · OpenAlex · Extracted main text
The synthetic difference-in-differences method provides an efficient method to estimate a causal effect with a latent factor model. However, it relies on the use of panel data. This paper presents an adaptation of the synthetic difference-in-differences method for repeated cross-sectional data. The treatment is considered to be at the group level so that it is possible to aggregate data by group to compute the two types of synthetic difference-in-differences weights on these aggregated data. Then, I develop and compute a third type of weight that accounts for the different number of observations in each cross-section. Simulation results show that the performance of the synthetic difference-in-differences estimator is improved when using the third type of weights on repeated cross-sectional data.
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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 | D. Arkhangelsky, S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager (2021) Synthetic difference-in-differences | 1.000 | 6 | 3 | 100% |
| 2 | Y. Xu (2017) Generalized synthetic control method: Causal inference with interactive fixed effects models | 0.511 | 2 | 1 | 100% |
| 3 | A. Abadie, A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.405 | 1 | 1 | 100% |
| 4 | J. Bai (2009) Panel data models with interactive fixed effects | 0.405 | 1 | 1 | 100% |
| 5 | S. Kranz (2022) Synthetic difference-in-differences with time-varying covariates, 2022 | 0.405 | 1 | 1 | 100% |
| 6 | Z. Porreca (2022) Synthetic difference-in-differences estimation with staggered treatment timing | 0.405 | 1 | 1 | 100% |
| 7 | J. Roth (2022) Pretest with caution: Event-study estimates after testing for parallel trends | 0.405 | 1 | 1 | 100% |
Showing the top 7 of 7 scored citations.