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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Пространствен панелен модел (фиксирани/случайни ефекти)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Пространствен анализ | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2014 | 2019 |
| Създател≠ | Elhorst; Lee & Yu | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Spatial econometric panel model | Linear regression |
| Основополагащ източник≠ | Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | spatial panel FE/RE, spatial econometric panel, spatial lag/error panel, Uzamsal Panel Modeli (Spatial Panel FE/RE) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани≠ | 4 | 5 |
| Резюме≠ | The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (2014) and Lee & Yu (2010). | 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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