Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Обобщени най-малки квадрати (ОНК)× | Метод на най-малките квадрати (МНК)× | Обобщен метод на най-малките квадрати за панелни данни (Panel GLS)× | |
|---|---|---|---|
| Област≠ | Статистика | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1935 | 2019 | 1935 / developed for panels 1980s–1990s |
| Създател≠ | Alexander Craig Aitken | Wooldridge (textbook treatment); classical least squares | Aitken (1935); extended to panel data by Baltagi and others |
| Тип≠ | Linear estimator | Linear regression | Generalized linear regression |
| Основополагащ източник≠ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Други названия≠ | GLS, Aitken estimator, EGLS, feasible GLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel |
| Свързани≠ | 3 | 5 | 3 |
| Резюме≠ | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. | 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). | Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified. |
| ScholarGateНабор от данни ↗ |
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