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