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| Panel MGWR (Panel Multiscale Geographically Weighted Regression)× | Modello di Errore Spaziale per Dati Panel× | |
|---|---|---|
| Campo | Analisi spaziale | Analisi spaziale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2017-2020 | 1988 / 2003 |
| Ideatore≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | Anselin (1988); extended to panels by Elhorst (2003, 2014) |
| Tipo≠ | Spatially varying coefficient panel regression | Spatial econometric panel model |
| Fonte seminale≠ | Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 |
| Alias | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | panel SEM, spatial error panel model, panel spatial autocorrelation error model, SEM panel |
| Correlati | 5 | 5 |
| Sintesi≠ | Panel MGWR extends Multiscale Geographically Weighted Regression to repeated-observations (panel) data, allowing each predictor to operate at its own spatial bandwidth while controlling for unit-specific or time-specific fixed effects. It is used when both spatial heterogeneity and temporal structure matter simultaneously. | 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. |
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