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| Modello Bayesiano a Ritardo Spaziale× | Modello Bayesiano dell'Errore Spaziale× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1997 | 1988 (classical SEM); 2009 (Bayesian formulation) |
| Ideatore≠ | LeSage (1997); fully elaborated in LeSage & Pace (2009) | LeSage & Pace (Bayesian treatment); Anselin (classical SEM) |
| Tipo | Bayesian spatial regression | Bayesian spatial regression |
| Fonte seminale | 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 SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM | Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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. |
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