Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskais laika panelis modelis× | Ģeogrāfiski svērtā regresija (GWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2003–2014 | 2002 |
| Autors≠ | J. Paul Elhorst | 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 | ST-SPM, spatiotemporal panel model, space-time panel econometrics, dynamic spatial panel model | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Space-Time Spatial Panel Model extends standard spatial panel econometrics to jointly account for cross-sectional spatial dependence, temporal autocorrelation, and unit-level heterogeneity. It allows outcomes in one location and time period to be influenced by outcomes in neighboring locations and by the location's own past, making it the canonical framework for dynamic spatiotemporal panel data analysis. | 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. |
| ScholarGateDatu kopa ↗ |
|
|