Comparer des méthodes

	Modèle de Vector Autoregression (VAR)	Modèle ARIMA (Autoregressive Integrated Moving Average)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2005	2015
Auteur d'origine	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Box & Jenkins (Box-Jenkins methodology)
Type	Multivariate time-series model	Univariate time-series model
Source fondatrice	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Alias	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Apparentées	4	5
Résumé	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).	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).