Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesiansk rumslig felmodell× | Bayesiansk spatial Durbin-modell× | |
|---|---|---|
| Ämnesområde | Rumslig analys | Rumslig analys |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1988 (classical SEM); 2009 (Bayesian formulation) | 2009 |
| Upphovsperson≠ | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) | LeSage & Pace |
| Typ | Bayesian spatial regression | Bayesian spatial regression |
| Ursprungskälla | 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 |
| Alias | Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model | Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | 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 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. |
| ScholarGateDatamängd ↗ |
|
|