Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственная модель Дарбина (SDM)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Пространственный анализ | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2009 | 2019 |
| Автор метода≠ | LeSage & Pace | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Spatial regression model | Linear regression |
| Основополагающий источник≠ | 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 |
| Другие названия≠ | SDM, spatial mixed model, uzamsal durbin modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | 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). |
| ScholarGateНабор данных ↗ |
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