Methoden vergleichen

	ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)	ARMA-Modell (Autoregressiver gleitender Durchschnitt)	Robuste verallgemeinerte Kleinste Quadrate (Robuste GLS)
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1970	1970	1936 / 1980
Urheber	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Typ	Time series forecasting model	Time series model	Robust linear regression
Wegweisende Quelle	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Aliasnamen	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Verwandt	6	5	5
Zusammenfassung	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	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.	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.