Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño de estudio de eventos (Estudio de eventos causal)× | Diseño de Regresión Discontinua (RDD)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2021 | 2008 |
| Autor original≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Tipo≠ | Dynamic causal panel regression | Quasi-experimental causal design |
| Fuente seminal≠ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Alias | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Relacionados | 5 | 5 |
| Resumen≠ | 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). | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
| ScholarGateConjunto de datos ↗ |
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