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| 空間パネルイベントスタディ× | 空間回帰不連続デザイン(Spatial RDD)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2010s–2020s | 2010s |
| 提唱者≠ | 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) |
| 種類 | Quasi-experimental causal inference | Quasi-experimental causal inference |
| 原典≠ | Sun, L., & Callaway, B. (2021). Difference-in-differences estimators of intertemporal treatment effects. arXiv:2109.10157. link ↗ | Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI ↗ |
| 別名 | spatial event study, spatial DiD event study, geo-panel event study, spatial panel ES | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design |
| 関連 | 4 | 4 |
| 概要≠ | 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. |
| ScholarGateデータセット ↗ |
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