Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Multi-period Event Study Design× | Panel Event Study× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1993 | 1990s–2020s (modern panel formulation) |
| Grondlegger≠ | Jacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021) | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| Type≠ | Quasi-experimental causal inference | Quasi-experimental / causal panel design |
| Oorspronkelijke bron≠ | Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83(4), 888-909. link ↗ | Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research. link ↗ |
| Aliassen | multi-period event study, dynamic event study, relative-time event study, leads-and-lags design | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | 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. | A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible. |
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