Vector autoregression (VAR) generalizes univariate autoregression (AR). A VAR process of order can be formulated as

,

where is an random vector, are fixed coefficient matrices, and is -dimensional white noise.

In econometrics, the VAR process is used to model linear interdependencies among multiple time series. This Demonstration serves as an analytical tool for modeling multiple time series data (mean adjusted) using the stable model . Outputs include model estimation, a residual check, and forecasts. You can also choose preferable estimation method (OLS or Yule–Walker estimator).