Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Conception d'étude d'événement en recherche sur l'éducation× | Modèle à effets fixes pour données de panel× | |
|---|---|---|
| Domaine≠ | Inférence causale | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1993 (general); 2000s–2010s (education applications) | 2014 |
| Auteur d'origine≠ | Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature | Hsiao (textbook treatment); within transformation of panel data |
| Type≠ | Quasi-experimental / causal inference | Panel data regression |
| Source fondatrice≠ | Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709. link ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Alias | event study, education event study, policy event study, dynamic difference-in-differences | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a causal estimate of the policy's impact and a transparent test of the parallel-trends assumption underpinning difference-in-differences. | 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). |
| ScholarGateJeu de données ↗ |
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