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| Σχεδιασμός Μελέτης Γεγονότος (Αιτιακός Σχεδιασμός Μελέτης Γεγονότος)× | Διαφορές διαφορών με διαδοχική εφαρμογή× | |
|---|---|---|
| Πεδίο | Αιτιακή Συμπερασματολογία | Αιτιακή Συμπερασματολογία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης | 2021 | 2021 |
| Δημιουργός≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Τύπος≠ | Dynamic causal panel regression | Quasi-experimental panel causal estimator |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator |
| Συναφείς≠ | 5 | 4 |
| Σύνοψη≠ | The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021). | Staggered Difference-in-Differences is a generalisation of DID for panel designs in which treatment is rolled out to different groups at different times. Introduced in the modern form by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it corrects the bias that classical two-way fixed-effects (TWFE) estimators suffer when treatment effects are heterogeneous across cohorts and over time. |
| ScholarGateΣύνολο δεδομένων ↗ |
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