Methoden vergleichen

	Robustes Autoregressives Modell	ARMA-Modell (Autoregressiver gleitender Durchschnitt)	Autoregressives Modell (AR)
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1986	1970	1970s (popularised 1976)
Urheber	Martin & Yohai (influential early work); broader robust time series literature	George E. P. Box and Gwilym M. Jenkins	George E. P. Box and Gwilym M. Jenkins
Typ	Robust time series model	Time series model	Time series model
Wegweisende Quelle	Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043
Aliasnamen	robust autoregression, outlier-robust AR, M-estimator AR, heavy-tail AR	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	AR model, AR(p) model, autoregression, AR process
Verwandt	6	5	6
Zusammenfassung	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.	The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.	An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.