Comparar métodos

	Modelo Espacial Durbin Bayesiano	Modelo de Erro Espacial Bayesiano
Área	Análise espacial	Análise espacial
Família	Regression model	Regression model
Ano de origem	2009	1988 (classical SEM); 2009 (Bayesian formulation)
Autor original	LeSage & Pace	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)
Tipo	Bayesian spatial regression	Bayesian spatial regression
Fonte seminal	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
Outros nomes	Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model
Relacionados	6	6
Resumo	The Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full posterior distributions for all parameters, quantifying uncertainty beyond what classical maximum-likelihood estimation offers.	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.