手法を比較

	自己回帰和分移動平均モデル (ARIMA Model)	構造的ベクトル自己回帰 (SVAR)
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	1970	1980
提唱者	George Box and Gwilym Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
種類	Time series forecasting model	Multivariate time series model
原典	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
別名	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
関連	6	5
概要	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.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.