Võrdle meetodeid

	Ruumiinstrumentaalmuutujad (Spatial IV / Spatial 2SLS)	Paneelpaneeliinstrumentaalmuutujad (Panel IV / 2SLS)
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	1988-1998	1978-1991
Looja	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)	Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)
Tüüp	Quasi-experimental causal inference with spatial dependence	Causal inference / panel regression
Algallikas	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.	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.
Rööpnimetused	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	Panel IV, Panel 2SLS, Within-IV, Fixed-Effects IV
Seotud	6	4
Kokkuvõte	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.