Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Spatial Fuzzy Regression Discontinuity Design× | Rumlig Instrumentalvariabel (Spatial IV / Spatial 2SLS)× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2015 | 1988-1998 |
| Ophavsperson≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework) |
| Type≠ | Quasi-experimental causal inference / IV-based spatial design | Quasi-experimental causal inference with spatial dependence |
| Oprindelig kilde≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17(1), 99-121. DOI ↗ |
| Aliasser | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV |
| Relaterede≠ | 5 | 6 |
| Resumé≠ | 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 Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighboring units, then applies two-stage least squares to recover unbiased causal estimates in the presence of both endogenous regressors and spatial autocorrelation. |
| ScholarGateDatasæt ↗ |
|
|