Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Variablat Instrumentale Spatiale (IV Spatiale / 2SLS Spatiale)× | Analiza Spaciale e Ndikimit Kauzal× | |
|---|---|---|
| Fusha | Inferenca kauzale | Inferenca kauzale |
| Familja | Regression model | Regression model |
| Viti i origjinës≠ | 1988-1998 | 2010s (codified) |
| Krijuesi≠ | Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework) | Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988) |
| Lloji≠ | Quasi-experimental causal inference with spatial dependence | Quasi-experimental causal inference with spatial data |
| Burimi themelues≠ | 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 ↗ | Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126. DOI ↗ |
| Emërtime të tjera | Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV | spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation |
| Të lidhura≠ | 6 | 4 |
| Përmbledhja≠ | 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. | Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas. |
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