So sánh phương pháp

	Mô hình AR mạnh mẽ	OLS mạnh mẽ (OLS với sai số chuẩn mạnh mẽ)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1986	1980
Người khởi xướng	Martin & Yohai (influential early work); broader robust time series literature	Halbert White
Loại	Robust time series model	Linear regression with robust inference
Công trình gốc	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Tên gọi khác	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Liên quan	6	6
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 OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.