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| Test del Placebo Spaziale× | Variabili Strumentali Spaziali (Spatial IV / Spatial 2SLS)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2000s–2010s | 1988-1998 |
| Ideatore≠ | Developed organically in spatial econometrics and geographic RDD literature; prominent use in Dell (2010) and related work | Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework) |
| Tipo≠ | Falsification / robustness check | Quasi-experimental causal inference with spatial dependence |
| Fonte seminale≠ | Buonanno, P., Montolio, D., & Vanin, P. (2009). Does Social Capital Reduce Crime? Journal of Law and Economics, 52(1), 145-170. 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 ↗ |
| Alias | geographic placebo test, spatial falsification test, spatial robustness check, geographic spillover test | Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV |
| Correlati≠ | 4 | 6 |
| Sintesi≠ | A spatial placebo test is a falsification check used in geographic or spatial causal-inference studies. The analyst applies the same estimation procedure to spatial units, boundaries, or zones where no treatment effect should exist — fake borders, shifted cutoffs, or buffer areas beyond spillover range — and checks whether a spurious effect emerges. A non-significant result in the placebo region supports the credibility of the main causal estimate. | 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. |
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