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| Differenza-in-differenze scaglionata× | Modello a Effetti Fissi per Dati Panel× | |
|---|---|---|
| Campo≠ | Inferenza causale | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2021 | 2014 |
| Ideatore≠ | Callaway & Sant'Anna; Sun & Abraham | Hsiao (textbook treatment); within transformation of panel data |
| Tipo≠ | Quasi-experimental panel causal estimator | Panel data regression |
| Fonte seminale≠ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alias≠ | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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 Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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