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| Spatial Regression× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1988 | 2019 |
| Twórca≠ | Luc Anselin | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Spatial regression (cross-sectional) | Linear regression |
| Źródło pierwotne≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | spatial econometrics, spatial lag model, spatial error model, SAR / SEM | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. | 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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