Comparar métodos

	Spatial Instrumental Variables	Diseño de Discontinuidad por Regresión Espacial (Spatial RDD)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1988-1998	2010s
Autor original	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Tipo	Quasi-experimental causal inference with spatial dependence	Quasi-experimental causal inference
Fuente seminal	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.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Relacionados	6	4
Resumen	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.	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.