手法を比較

	ベクトル自己回帰（VAR）モデル	ARIMA（自己回帰和分移動平均）モデル	ベクトル誤差修正モデル（VECM）
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2005	2015	1987
提唱者	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Box & Jenkins (Box-Jenkins methodology)	Engle & Granger
種類	Multivariate time-series model	Univariate time-series model	Multivariate time-series model
原典	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	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
別名	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
関連	4	5	4
概要	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).	The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.