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Przestrzenne zmienne instrumentalne (Spatial IV / Spatial 2SLS)×Zmienne instrumentalne dla danych panelowych (Panel IV / 2SLS)×
DziedzinaWnioskowanie przyczynoweWnioskowanie przyczynowe
RodzinaRegression modelRegression model
Rok powstania1988-19981978-1991
TwórcaKelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)
TypQuasi-experimental causal inference with spatial dependenceCausal inference / panel regression
Źródło pierwotneKelejian, 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 ↗
Inne nazwySpatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IVPanel IV, Panel 2SLS, Within-IV, Fixed-Effects IV
Pokrewne64
PodsumowanieSpatial 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.
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ScholarGatePorównaj metody: Spatial Instrumental Variables · Panel Data Instrumental Variables. Pobrano 2026-06-18 z https://scholargate.app/pl/compare