Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Panel Multiscale Geographically Weighted Regression (Panel MGWR)× | Panel Ruimtelijk Durbin Model× | |
|---|---|---|
| Vakgebied | Ruimtelijke analyse | Ruimtelijke analyse |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2017-2020 | 2009–2010 |
| Grondlegger≠ | Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature | LeSage & Pace (2009); panel extension by Elhorst (2010) |
| Type≠ | Spatially varying coefficient panel regression | Spatial panel regression |
| Oorspronkelijke bron≠ | 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 |
| Aliassen | Panel MGWR, MGWR panel data, multiscale GWR panel, panel spatially varying coefficient model | SDM panel, spatial Durbin panel model, panel SDM, PSDM |
| Verwant | 5 | 5 |
| Samenvatting≠ | 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 Durbin Model (PSDM) extends the cross-sectional Spatial Durbin Model to panel data, capturing both spatial lag dependence in the outcome and spatial spillovers from neighbouring units' explanatory variables across multiple time periods. It simultaneously accounts for unobserved unit-specific and time-specific heterogeneity, making it one of the most comprehensive spatial panel specifications available. |
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