Comparer des méthodes

	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)	Modèle de Vector Autoregression (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970	2005
Auteur d'origine	George Box and Gwilym Jenkins	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Time series forecasting model	Multivariate time-series model
Source fondatrice	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Apparentées	6	4
Résumé	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).