Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Robust Generalized Least Squares (Robust GLS)× | Регресія звичайно найменших квадратів (ЗНК)× | Метод узагальненого найменшого відхилення (Panel GLS)× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1936 / 1980 | 2019 | 1935 / developed for panels 1980s–1990s |
| Автор методу≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Wooldridge (textbook treatment); classical least squares | Aitken (1935); extended to panel data by Baltagi and others |
| Тип≠ | Robust linear regression | Linear regression | Generalized linear regression |
| Основоположне джерело≠ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 | 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 |
| Інші назви | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-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 |
| Пов'язані≠ | 5 | 5 | 3 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
|
|
|