Comparer des méthodes

	Modèle SAC Spatial	Modèle de Durbin spatial (SDM)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2009	2009
Auteur d'origine	James LeSage & R. Kelley Pace	LeSage & Pace
Type	Combined spatial dependence regression model	Spatial regression model
Source fondatrice	LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. ISBN: 978-1-4200-6424-7	LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.
Alias	SARAR Model, Spatial Autoregressive Model with Autoregressive Disturbances, Cliff-Ord Combined Model, Uzamsal Otoregresif Birleşik Model	SDM, spatial mixed model, uzamsal durbin modeli
Apparentées	3	5
Résumé	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 Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases.