So sánh phương pháp

	Mô hình Trung bình Trượt Mạnh mẽ (MA)	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	1979–2009	1980
Người khởi xướng	Denby & Martin (1979); Muler, Pena & Yohai (2009)	Halbert White
Loại	Robust time series model	Linear regression with robust inference
Công trình gốc	Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Tên gọi khác	robust MA, robust moving average, M-estimation MA, bounded-influence MA	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Liên quan	6	6
Tóm tắt	The Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA.	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.