Comparar métodos

	Modelo SAC Espacial	Modelo de Error Espacial (SEM)
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	2009	1988
Autor original	James LeSage & R. Kelley Pace	Anselin
Tipo	Combined spatial dependence regression model	Spatial regression (spatially autocorrelated errors)
Fuente seminal	LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. ISBN: 978-1-4200-6424-7	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	SARAR Model, Spatial Autoregressive Model with Autoregressive Disturbances, Cliff-Ord Combined Model, Uzamsal Otoregresif Birleşik Model	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Relacionados	3	5
Resumen	The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estimating two distinct spatial autoregressive parameters — one capturing substantive spatial interaction among outcomes and another capturing residual spatial correlation among disturbances.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.