Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Dynamisk hendelsesstudie-design× | Dynamisk differanse-i-differanser× | |
|---|---|---|
| Fagfelt | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 2021 (canonical treatment); practice since 1990s) | 2021 |
| Opphavsperson≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics | Callaway & Sant'Anna; Sun & Abraham |
| Type≠ | Quasi-experimental / causal inference | Causal inference / quasi-experimental |
| Opprinnelig kilde≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Alias | dynamic DiD, lead-lag event study, relative-time event study, event-time regression | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Relaterte≠ | 3 | 4 |
| Sammendrag≠ | The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods. | 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. |
| ScholarGateDatasett ↗ |
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