Salīdzināt metodes

	Robusts autoregresīvais modelis	Robustā OLS (OLS ar robustām standarta kļūdām)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1986	1980
Autors	Martin & Yohai (influential early work); broader robust time series literature	Halbert White
Tips	Robust time series model	Linear regression with robust inference
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	6
Kopsavilkums	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.