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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle de Panel Spatial Global× | Modèle de Durbin spatial (SDM)× | |
|---|---|---|
| Domaine | Analyse spatiale | Analyse spatiale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2003-2010 | 2009 |
| Auteur d'origine≠ | Elhorst, J. P.; Lee, L. F. & Yu, J. | LeSage & Pace |
| Type≠ | Spatial panel regression | Spatial regression model |
| Source fondatrice≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| Alias≠ | spatial panel model with global weights, global spatial panel regression, spatial panel data model, GSPM | SDM, spatial mixed model, uzamsal durbin modeli |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | The Global Spatial Panel Model extends panel data regression by incorporating a global spatial weights matrix that links every location to every other location simultaneously. It jointly accounts for cross-sectional spatial dependence, time-series dynamics, and individual fixed or random effects, making it the standard workhorse for panel data when spatial spillovers operate across the full study region. | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. |
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