Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ģeogrāfiskā regresijas atšķirība× | Sintētiskā starpību starpībās metode× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2010 | 2021 |
| Autors≠ | Melissa Dell and colleagues | Arkhangelsky, Athey, Hirshberg, Imbens, and Wager |
| Tips≠ | Spatial quasi-experiment | Treatment-effect estimation |
| Pirmavots≠ | Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911. link ↗ | Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118. DOI ↗ |
| Citi nosaukumi | Spatial RD, Geographic RDD | Synthetic DID, SDID |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena. | Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted combinations of controls to match treated units' pre-treatment trends and levels. This yields more precise and robust estimates than classical DiD or synthetic control. |
| ScholarGateDatu kopa ↗ |
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