Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskā regresija (telpiskā nobīdes un telpiskās kļūdas modeļi)× | Šķietami nesaistītas regresijas (SUR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 | 1962 |
| Autors≠ | Luc Anselin | Arnold Zellner |
| Tips≠ | Spatial regression (cross-sectional) | System regression (multi-equation) |
| Pirmavots≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. DOI ↗ | Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368. DOI ↗ |
| Citi nosaukumi≠ | spatial econometrics, spatial lag model, spatial error model, SAR / SEM | SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR) |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term. | Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS. |
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