Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Робастни обобщени най-малки квадрати (Robust GLS)× | Обобщени най-малки квадрати (ОНК)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|---|
| Област≠ | Иконометрия | Статистика | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1936 / 1980 | 1935 | 2019 |
| Създател≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Alexander Craig Aitken | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Robust linear regression | Linear estimator | Linear regression |
| Основополагащ източник≠ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 | 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 |
| Други названия≠ | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS | GLS, Aitken estimator, EGLS, feasible GLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани≠ | 5 | 3 | 5 |
| Резюме≠ | Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure. | 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). |
| ScholarGateНабор от данни ↗ |
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