Comparer des méthodes

	Régression spatiale (modèles de retard spatial et d'erreur spatiale)	Régression apparemment non liée (SUR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1988	1962
Auteur d'origine	Luc Anselin	Arnold Zellner
Type	Spatial regression (cross-sectional)	System regression (multi-equation)
Source fondatrice	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.	Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.
Alias	spatial econometrics, spatial lag model, spatial error model, SAR / SEM	SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)
Apparentées	5	5
Résumé	Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term.	Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.