Comparer des méthodes

	Modèle Bayésien d'Erreur Spatiale	Modèle d'erreur spatiale (SEM)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1988 (classical SEM); 2009 (Bayesian formulation)	1988
Auteur d'origine	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	Anselin
Type	Bayesian spatial regression	Spatial regression (spatially autocorrelated errors)
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.
Alias	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Apparentées	6	5
Résumé	The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesian inference rather than point estimates.	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.