Abstract 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 three quarterly variables. The results show that the predictive ability typically benefits from using mixed-frequency data, and that improvement 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.

中文翻译：

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具有稳态先验的柔性混合频率矢量自回归

摘要我们提出了一种用于混合频率数据的贝叶斯向量自回归（VAR）模型。我们的模型基于VAR的均值调整参数化，并允许对包含变量的“稳态”（无条件均值）进行明确的先验。基于文献的最新发展，我们讨论了模型的扩展，这些扩展提高了建模方法的灵活性。这些扩展包括在稳态参数之前先进行分层收缩，并使用随机波动率对异方差建模。我们将提出的模型用于由10个每月变量和3个季度变量组成的美国数据的预测评估中。结果表明，预测能力通常受益于使用混合频率数据，并且对于每月和每季度变量都可以获得改善。