Linganisha mbinu

	Utafiti wa Tukio la Paneli ya Anga	Muundo wa Usambazaji wa Regression wa Kijiografia (Spatial RDD)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010s–2020s	2010s
Mwanzilishi	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)
Aina	Quasi-experimental causal inference	Quasi-experimental causal inference
Chanzo asilia	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.
Majina mbadala	spatial event study, spatial DiD event study, geo-panel event study, spatial panel ES	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Zinazohusiana	4	4
Muhtasari	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.