Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle de Durbin spatial (SDM)× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine≠ | Analyse spatiale | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2009 | 2019 |
| Auteur d'origine≠ | LeSage & Pace | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Spatial regression model | Linear regression |
| Source fondatrice≠ | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | SDM, spatial mixed model, uzamsal durbin modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | 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). |
| ScholarGateJeu de données ↗ |
|
|