방법 비교

	ARIMA 모형 (자기회귀 누적 이동평균)	구조적 벡터 자기회귀 (SVAR)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1970	1980
창시자	George Box and Gwilym Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
유형	Time series forecasting model	Multivariate time series model
원전	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.
별칭	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
관련	6	5
요약	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.