Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskais kļūdu modelis (SEM)× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare≠ | Telpiskā analīze | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1988 | 2019 |
| Autors≠ | Anselin | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Spatial regression (spatially autocorrelated errors) | Linear regression |
| Pirmavots≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Citi nosaukumi | SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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