Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Пространствен лаг модел (SAR / Spatial Autoregressive)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Пространствен анализ | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1988 | 2019 |
| Създател≠ | Anselin (textbook formalisation); LeSage & Pace | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Spatial autoregressive regression | Linear regression |
| Основополагащ източник≠ | 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 |
| Други названия | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. | 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 от данни ↗ |
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