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| Panel event study× | Regression Discontinuity Design (RDD)× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2021 | 2008 |
| Ophavsperson≠ | Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021) | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Type≠ | Causal inference / quasi-experimental panel design | Quasi-experimental causal design |
| Oprindelig kilde≠ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Aliasser≠ | panel event study, event-study DiD, staggered event study, difference-in-differences event study | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
| ScholarGateDatasæt ↗ |
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