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.

翻译：由于经济环境的变化不确定,Gaussian结构冲击无法解释衰退和危机期间的宏观经济下滑。有证据表明宏观经济变量的分布偏斜和严重尾巴。在本文中,我们通过扩展矢量自动回归模型(VAR)为文献贡献力量,以更现实地假设宏观经济变量的多变分布。我们建议对VAR模型的错误术语使用一般等级的超双曲扭曲学生T分布与随机性波动,允许我们考虑到扭曲和重尾巴。提供了使用Gibbs取样器进行Bayesian推断和模型选择的工具。在一项实证研究中,我们提出了每月宏观经济变量的扭曲和重尾巴的证据。我们的分析还给出了一个明确的信息,即对于衰退和危机期间的更好的预测,应该将偏差考虑在内。