Confronta i metodi

	Modello Autoregressivo Robusto	Robust Generalized Least Squares (Robust GLS)
Campo	Econometria	Econometria
Famiglia	Regression model	Regression model
Anno di origine	1986	1936 / 1980
Ideatore	Martin & Yohai (influential early work); broader robust time series literature	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Tipo	Robust time series model	Robust linear regression
Fonte seminale	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
Alias	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
Correlati	6	5
Sintesi	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.