方法对比

	Bayesian Spatial Lag Model	贝叶斯空间误差模型
领域	空间分析	空间分析
方法族	Regression model	Regression model
起源年份	1997	1988 (classical SEM); 2009 (Bayesian formulation)
提出者	LeSage (1997); fully elaborated in LeSage & Pace (2009)	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)
类型	Bayesian spatial regression	Bayesian spatial regression
开创性文献	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
别名	Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model
相关	5	6
摘要	The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho.	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.