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| Multi-periode Event Study Design× | Dynamisk Difference-in-Differences× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1993 | 2021 |
| Ophavsperson≠ | Jacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Type≠ | Quasi-experimental causal inference | Causal inference / quasi-experimental |
| Oprindelig kilde≠ | Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83(4), 888-909. link ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Aliasser | multi-period event study, dynamic event study, relative-time event study, leads-and-lags design | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Relaterede≠ | 3 | 4 |
| Resumé≠ | The multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficients rather than a single average, it reveals how effects build up, fade, or remain stable over time — and allows formal tests of pre-treatment parallel trends across many periods simultaneously. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
| ScholarGateDatasæt ↗ |
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