  With uncertain changes of the economic environment, macroeconomic downturns
during recessions and crises can hardly be explained by a Gaussian structural
shock. There is evidence that the distribution of macroeconomic variables is
skewed and heavy tailed. In this paper, we contribute to the literature by
extending a vector autoregression (VAR) model to account for a more realistic
assumption of the multivariate distribution of the macroeconomic variables. We
propose a general class of generalized hyperbolic skew Student's t distribution
with stochastic volatility for the error term in the VAR model that allows us
to take into account skewness and heavy tails. Tools for Bayesian inference and
model selection using a Gibbs sampler are provided. In an empirical study, we
present evidence of skewness and heavy tails for monthly macroeconomic
variables. The analysis also gives a clear message that skewness should be
taken into account for better predictions during recessions and crises.
