পদ্ধতির তুলনা করুন

	ARIMA মডেল (অটোরিগ্রেসিভ ইন্টিগ্রেটেড মুভিং অ্যাভারেজ)	অটো রিগ্রেসিভ মুভিং অ্যাভারেজ (ARMA) মডেল	কাঠামোগত ভেক্টর অটোরিগ্রেশন (SVAR)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1970	1970	1980
প্রবর্তক	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
ধরন	Time series forecasting model	Time series model	Multivariate time series model
মৌলিক উৎস	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	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.
অপর নাম	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.	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.