Salīdzināt metodes

	Telpiskā izplūdušās regresijas diskontinuitātes metode	Telpiskie instrumentālie mainīgie (Spatial IV / Spatial 2SLS)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015	1988-1998
Autors	Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)
Tips	Quasi-experimental causal inference / IV-based spatial design	Quasi-experimental causal inference with spatial dependence
Pirmavots	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	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.
Citi nosaukumi	Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV
Saistītās	5	6
Kopsavilkums	Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD.	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.