Salīdzināt metodes

	Robusts autoregresīvais modelis	Robustais Vektora kļūdu labojumu modelis (Robust VECM)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1986	1997–2001
Autors	Martin & Yohai (influential early work); broader robust time series literature	Sakata & White (1998); Lucas (1997) — robust cointegrated system estimation
Tips	Robust time series model	Robust multivariate time-series model
Pirmavots	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	Caner, M., & Kilian, L. (2001). Size distortions of tests of the null hypothesis of stationarity: Evidence and implications for the PPP debate. Journal of International Money and Finance, 20(5), 639-657.
Citi nosaukumi	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	robust VECM, outlier-robust VECM, robust cointegration model, robust VEC model
Saistītās	6	1
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 VECM extends the classical Vector Error Correction Model by replacing ordinary least squares estimation with outlier-resistant procedures — such as M-estimators, S-estimators, or least trimmed squares — so that cointegration relationships and short-run adjustment dynamics are estimated reliably even when the multivariate time series contains outliers, structural breaks, or heavy-tailed innovations.