Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Odhaditel prostorového párování× | Prostorové rozdíly v rozdílech× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2000s–2010s | 2015 |
| Tvůrce≠ | Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature | Delgado & Florax |
| Typ≠ | Quasi-experimental causal inference | Quasi-experimental estimator |
| Původní zdroj≠ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ | Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40. DOI ↗ |
| Další názvy | geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching | Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar |
| Příbuzné≠ | 6 | 3 |
| Shrnutí≠ | The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by spatial proximity, the method controls for location-specific confounders that standard matching ignores. | Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms. |
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