Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Tofauti-katika-Tofauti Zilizopangwa Kimakundi× | Muundo wa Utafiti wa Tukio (Utafiti wa Tukio wa Kiusababishi)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili | 2021 | 2021 |
| Mwanzilishi≠ | Callaway & Sant'Anna; Sun & Abraham | Sun & Abraham (2021); Callaway & Sant'Anna (2021) |
| Aina≠ | Quasi-experimental panel causal estimator | Dynamic causal panel regression |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala≠ | 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 |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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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