Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskās regresijas paneļa analīze× | Telpiskais kļūdu modelis (SEM)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988-2014 | 1988 |
| Autors≠ | Anselin, Elhorst, and colleagues in spatial econometrics | Anselin |
| Tips≠ | Spatial panel regression | Spatial regression (spatially autocorrelated errors) |
| Pirmavots≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. ISBN: 978-3642403408 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Citi nosaukumi | spatial panel model, panel spatial econometrics, spatial panel data regression, PSR | SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error) |
| 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 Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares. |
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