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| Globālais telpiskais paneļa modelis× | Ģeogrāfiski svērtā regresija (GWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2003-2010 | 2002 |
| Autors≠ | Elhorst, J. P.; Lee, L. F. & Yu, J. | Fotheringham, Brunsdon & Charlton |
| Tips≠ | Spatial panel regression | Local spatial regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi | spatial panel model with global weights, global spatial panel regression, spatial panel data model, GSPM | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. | 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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