The bayes prefix now supports the var command to fit Bayesian vector autoregressive (VAR) models.
VAR models study relationships between multiple time series by including lags of outcome variables as model predictors. These models are known to have many parameters: with K outcome variables and p lags, there are at least p(K^2+\nn1) parameters. Reliable estimation of the model parameters can be challenging, especially with small datasets.
Bayesian VAR models overcome these challenges by incorporating prior information about model parameters to stabilize parameter estimation.