Сравнение методов

	Bayesian Spatial Durbin Model	Пространственная модель ошибок (Spatial Error Model, SEM)
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	2009	1988
Автор метода	LeSage & Pace	Anselin
Тип	Bayesian spatial regression	Spatial regression (spatially autocorrelated errors)
Основополагающий источник	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.
Другие названия	Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Связанные	6	5
Сводка	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 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.