In a VAR model, each variable is modeled as a linear function of past lags of itself and past lags of other variables in the system.
VAR models differ from univariate autoregressive models because they allow feedback to occur between the variables in the model.
An estimated VAR model can be used for forecasting, and the quality of the forecasts can be judged, in ways that are completely analogous to the methods used in univariate autoregressive modelling.
Using an autoregressive (AR) modeling approach, the vector autoregression (VAR) method examines the relationships between multiple time series variables at different time steps.
The VAR model’s parameter specification involves providing the order of the AR(p) model, which represents the number of lagged values included in the analysis.
