Võrdle meetodeid

	Ruumiline paneeli sündmuseuuring	Ruumi-regressioonkatkestusdisain (Spatial RDD)
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2010s–2020s	2010s
Looja	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)
Tüüp	Quasi-experimental causal inference	Quasi-experimental causal inference
Algallikas	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.
Rööpnimetused	spatial event study, spatial DiD event study, geo-panel event study, spatial panel ES	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Seotud	4	4
Kokkuvõte	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.