Compară metode

	Model ARIMA (Autoregresiv Integrat Medie Mobilă)	Testul de cauzalitate Granger	Vector Autoregresiv Structural (SVAR)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1970	1969	1980
Autorul original	George Box and Gwilym Jenkins	Clive W. J. Granger	Sims (1980); identification schemes by Blanchard & Quah (1989)
Tip	Time series forecasting model	Causality test (F-test on VAR)	Multivariate time series model
Sursa seminală	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Denumiri alternative	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	Granger test, GC test, predictive causality test, Granger non-causality test	SVAR, structural vector autoregression, identified VAR, structural VAR model
Înrudite	6	5	5
Rezumat	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.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.	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.