  We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency
data. Our model is based on the mean-adjusted parametrization of the VAR and
allows for an explicit prior on the 'steady states' (unconditional means) of
the included variables. Based on recent developments in the literature, we
discuss extensions of the model that improve the flexibility of the modeling
approach. These extensions include a hierarchical shrinkage prior for the
steady-state parameters, and the use of stochastic volatility to model
heteroskedasticity. We put the proposed model to use in a forecast evaluation
using US data consisting of 10 monthly and 3 quarterly variables. The results
show that the predictive ability typically benefits from using mixed-frequency
data, and that improvements can be obtained for both monthly and quarterly
variables. We also find that the steady-state prior generally enhances the
accuracy of the forecasts, and that accounting for heteroskedasticity by means
of stochastic volatility usually provides additional improvements, although not
for all variables.
