  Motivated by the proliferation of extensive macroeconomic and health datasets
necessitating accurate forecasts, a novel approach is introduced to address
Vector Autoregressive (VAR) models. This approach employs the global-local
shrinkage-Wishart prior. Unlike conventional VAR models, where degrees of
freedom are predetermined to be equivalent to the size of the variable plus one
or equal to zero, the proposed method integrates a hyperprior for the degrees
of freedom to account for the uncertainty about the parameter values.
Specifically, a loss-based prior is derived to leverage information regarding
the data-inherent degrees of freedom. The efficacy of the proposed prior is
demonstrated in a multivariate setting for forecasting macroeconomic data, as
well as Dengue infection data.
