Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная авторегрессионная модель× | Робастный обобщенный метод наименьших квадратов (Robust GLS)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1986 | 1936 / 1980 |
| Автор метода≠ | Martin & Yohai (influential early work); broader robust time series literature | Aitken (GLS theory, 1936); White (robust covariance, 1980) |
| Тип≠ | Robust time series model | Robust linear regression |
| Основополагающий источник≠ | Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818. DOI ↗ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 |
| Другие названия | robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics. | 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. |
| ScholarGateНабор данных ↗ |
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