השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	אוטורגרסיה וקטורית מבנית (SVAR)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1970	1980
הוגה השיטה	George Box and Gwilym Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
סוג	Time series forecasting model	Multivariate time series model
מקור מכונן	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.
כינויים	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
קשורות	6	5
תקציר	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.