Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Variabile Instrumentale Spațiale (Spatial IV / Spatial 2SLS)× | Variabile Instrumentale pentru Date Paneli (Panel IV / 2SLS)× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1988-1998 | 1978-1991 |
| Autorul original≠ | Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework) | Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991) |
| Tip≠ | Quasi-experimental causal inference with spatial dependence | Causal inference / panel regression |
| Sursa seminală≠ | 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 ↗ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297. DOI ↗ |
| Denumiri alternative | Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV | Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV |
| Înrudite≠ | 6 | 4 |
| Rezumat≠ | 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. | Panel data instrumental variables combines the bias-correcting power of instrumental variables (IV) with the within-unit variation exploited by panel data methods. It addresses endogeneity — omitted variables, reverse causation, or measurement error — in longitudinal settings where observations are repeated across units and time. Seminal contributions come from Hausman (1978) on specification testing and Arellano and Bond (1991) on GMM-based panel IV. |
| ScholarGateSet de date ↗ |
|
|