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| Desain Studi Peristiwa Dinamis× | Difference-in-Differences Dinamis× | |
|---|---|---|
| Bidang | Inferensi Kausal | Inferensi Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2021 (canonical treatment); practice since 1990s) | 2021 |
| Pencetus≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics | Callaway & Sant'Anna; Sun & Abraham |
| Tipe≠ | Quasi-experimental / causal inference | Causal inference / quasi-experimental |
| Sumber perintis≠ | 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 |
| Terkait≠ | 3 | 4 |
| Ringkasan≠ | 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. |
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