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| Panel Spatial Error Model× | Geographisch gewichtete Regression (GWR)× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1988 / 2003 | 2002 |
| Urheber≠ | Anselin (1988); extended to panels by Elhorst (2003, 2014) | Fotheringham, Brunsdon & Charlton |
| Typ≠ | Spatial econometric panel model | Local spatial regression |
| Wegweisende Quelle≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Aliasnamen | panel SEM, spatial error panel model, panel spatial autocorrelation error model, SEM panel | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | The Panel Spatial Error Model (panel SEM) extends the classical spatial error model to panel data, allowing spatial dependence to enter through the error term across cross-sectional units over multiple time periods. It accounts for spatially correlated omitted variables without imposing a substantive spatial spillover in the outcome itself. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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