So sánh phương pháp

	Mô hình AR mạnh mẽ	Tổng bình phương nhỏ nhất tổng quát mạnh mẽ (Robust GLS)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1986	1936 / 1980
Người khởi xướng	Martin & Yohai (influential early work); broader robust time series literature	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Loại	Robust time series model	Robust linear regression
Công trình gốc	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Tên gọi khác	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
Liên quan	6	5
Tóm tắt	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.