The
vector autoregression (
VAR) is an
econometric model used to capture the linear interdependencies among multiple
time series. VAR models generalize the univariate
autoregressive model (AR model) by allowing for more than one evolving variable. All variables in a VAR are treated symmetrically in a structural sense (although the estimated quantitative response coefficients will not in general be the same); each variable has an equation explaining its evolution based on its own
lags and the lags of the other model variables. VAR modeling does not require as much knowledge about the forces influencing a variable as do
structural models with
simultaneous equations: The only prior knowledge required is a list of variables which can be hypothesized to affect each other intertemporally.