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| Spatial Counterfactual Impact Evaluation (SCIE)× | Geografisk vægtede regression (GWR)× | |
|---|---|---|
| Fagområde≠ | Kausal inferens | Rumlig analyse |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2010s | 2002 |
| Ophavsperson≠ | Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics | Fotheringham, Brunsdon & Charlton |
| Type≠ | Quasi-experimental / causal inference | Local spatial regression |
| Oprindelig kilde≠ | Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Aliasser | SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Relaterede | 5 | 5 |
| Resumé≠ | Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
| ScholarGateDatasæt ↗ |
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