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| Dynamisches Event-Study-Design× | Differenz-in-Differenzen (DiD)× | |
|---|---|---|
| Fachgebiet≠ | Kausale Inferenz | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2021 (canonical treatment); practice since 1990s) | 1994 |
| Urheber≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Typ≠ | Quasi-experimental / causal inference | Causal inference / panel regression |
| Wegweisende Quelle≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Aliasnamen≠ | dynamic DiD, lead-lag event study, relative-time event study, event-time regression | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
| ScholarGateDatensatz ↗ |
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