Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskais SAC modelis× | Telpiskais Durbina modelis (SDM)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 2009 | 2009 |
| Autors≠ | James LeSage & R. Kelley Pace | LeSage & Pace |
| Tips≠ | Combined spatial dependence regression model | Spatial regression model |
| Pirmavots≠ | LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. ISBN: 978-1-4200-6424-7 | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| Citi nosaukumi≠ | SARAR Model, Spatial Autoregressive Model with Autoregressive Disturbances, Cliff-Ord Combined Model, Uzamsal Otoregresif Birleşik Model | SDM, spatial mixed model, uzamsal durbin modeli |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estimating two distinct spatial autoregressive parameters — one capturing substantive spatial interaction among outcomes and another capturing residual spatial correlation among disturbances. | 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. |
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