Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Romlig Fuzzy Regression Diskontinuitetsdesign× | Romlig regresjonsdiskontinuitetsdesign (Spatial RDD)× | |
|---|---|---|
| Fagfelt | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 2015 | 2010s |
| Opphavsperson≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015) |
| Type≠ | Quasi-experimental causal inference / IV-based spatial design | Quasi-experimental causal inference |
| Opprinnelig kilde≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI ↗ |
| Alias | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design |
| Relaterte≠ | 5 | 4 |
| Sammendrag≠ | Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD. | 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. |
| ScholarGateDatasett ↗ |
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