方法对比
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| 空间反事实影响评估 (SCIE)× | 地理加权回归 (GWR)× | |
|---|---|---|
| 领域≠ | 因果推断 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010s | 2002 |
| 提出者≠ | Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics | Fotheringham, Brunsdon & Charlton |
| 类型≠ | Quasi-experimental / causal inference | Local spatial regression |
| 开创性文献≠ | 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 |
| 别名 | SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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