Comparar métodos

	Diseño de Discontinuidad de Regresión Difusa Multiperiódica	Diferencias en Diferencias Multiperíodo (DiD Escalonado)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2001 (fuzzy RD); multi-period extension ~2010s	2021
Autor original	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Callaway & Sant'Anna; Goodman-Bacon
Tipo	Quasi-experimental causal inference	Causal inference / panel regression
Fuente seminal	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD
Relacionados	4	5
Resumen	Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.	Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresses the bias that arises when conventional two-way fixed-effects regressions are applied to staggered adoption designs.