Comparer des méthodes

	Étude d'événement sur panel spatial	Discontinuité de régression spatiale (Spatial RDD)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010s–2020s	2010s
Auteur d'origine	Synthesized from spatial econometrics and panel event-study literatures; formalized in applied work in the 2010s–2020s	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Type	Quasi-experimental causal inference	Quasi-experimental causal inference
Source fondatrice	Sun, L., & Callaway, B. (2021). Difference-in-differences estimators of intertemporal treatment effects. arXiv:2109.10157.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	spatial event study, spatial DiD event study, geo-panel event study, spatial panel ES	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Apparentées	4	4
Résumé	Spatial panel event study extends the classical panel event-study design to settings where units are geographically located and outcomes may spill over across space. By combining event-time indicators with spatial weights matrices, it estimates dynamic treatment effects while explicitly accounting for spatial autocorrelation, geographic spillovers, and cross-unit contamination that would bias conventional event studies.	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.