Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskās regresijas paneļa analīze× | Telpiskais Durbina modelis (SDM)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988-2014 | 2009 |
| Autors≠ | Anselin, Elhorst, and colleagues in spatial econometrics | LeSage & Pace |
| Tips≠ | Spatial panel regression | Spatial regression model |
| Pirmavots≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| Citi nosaukumi≠ | spatial panel model, panel spatial econometrics, spatial panel data regression, PSR | SDM, spatial mixed model, uzamsal durbin modeli |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and efficient estimates when observations are spatially correlated across units. | The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. |
| ScholarGateDatu kopa ↗ |
|
|