مقایسهٔ روش‌ها

	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	مدل خودرگرسیون برداری ساختاری (SVAR)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	1970	1980
پدیدآور	George Box and Gwilym Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
نوع	Time series forecasting model	Multivariate time series model
منبع بنیادین	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.
نام‌های دیگر	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
مرتبط	6	5
خلاصه	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.