Krahasoni metodat

	Modeli ARIMA (Autoregresiv i Integruar Mesatar Lëvizës)	Model ARMA (Autoregressive Moving Average)	Autoregresioni Vektoriale Strukturore (SVAR)
Fusha	Ekonometri	Ekonometri	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	1970	1970	1980
Krijuesi	George Box and Gwilym Jenkins	George E. P. Box and Gwilym M. Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
Lloji	Time series forecasting model	Time series model	Multivariate time series model
Burimi themelues	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.
Emërtime të tjera	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
Të lidhura	6	5	5
Përmbledhja	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.