השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	Autoregression Vector (VAR)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1970	1980
הוגה השיטה	George Box and Gwilym Jenkins	Christopher A. Sims
סוג	Time series forecasting model	Multivariate time-series model
מקור מכונן	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
כינויים	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	VAR, VAR model, vector autoregressive model, multivariate autoregression
קשורות	6	5
תקציר	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.	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.