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| Bayesian Spatial Durbin Model× | ベイズ空間誤差モデル× | |
|---|---|---|
| 分野 | 空間分析 | 空間分析 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2009 | 1988 (classical SEM); 2009 (Bayesian formulation) |
| 提唱者≠ | LeSage & Pace | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) |
| 種類 | Bayesian spatial regression | Bayesian spatial regression |
| 原典 | 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 |
| 別名 | 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 |
| 関連 | 6 | 6 |
| 概要≠ | 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. |
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