Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Izkliedētā starpības-starp-atšķirībām metode× | Dizains "notikumu pētījums" (cēloņsakarību notikumu pētījums)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 2021 | 2021 |
| Autors≠ | Callaway & Sant'Anna; Sun & Abraham | Sun & Abraham (2021); Callaway & Sant'Anna (2021) |
| Tips≠ | Quasi-experimental panel causal estimator | Dynamic causal panel regression |
| Pirmavots≠ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ |
| Citi nosaukumi≠ | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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). |
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