পদ্ধতির তুলনা করুন

	ARIMA মডেল (অটোরিগ্রেসিভ ইন্টিগ্রেটেড মুভিং অ্যাভারেজ)	গ্র্যাঞ্জার কার্যকারণ পরীক্ষা	কাঠামোগত ভেক্টর অটোরিগ্রেশন (SVAR)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1970	1969	1980
প্রবর্তক	George Box and Gwilym Jenkins	Clive W. J. Granger	Sims (1980); identification schemes by Blanchard & Quah (1989)
ধরন	Time series forecasting model	Causality test (F-test on VAR)	Multivariate time series model
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	5	5
সারসংক্ষেপ	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.