Comparer des méthodes

	Modèle de Durbin Spatial Global (SDM)	Global Spatial Error Model
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2009	1988
Auteur d'origine	Durbin (1960); adapted to spatial context by LeSage & Pace (2009)	Luc Anselin
Type	Spatial regression model	Spatial regression model
Source fondatrice	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322
Alias	SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag	SEM, spatial error model, spatial error regression, global SEM
Apparentées	5	5
Résumé	The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region.	The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.