具有稳态先验的灵活混合频率向量自回归
A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior
摘要：
本文提出了一种适用于混合频率数据的贝叶斯向量自回归（VAR）模型。我们的模型是基于VAR的均值调整参数化，并允许一个明确的“稳定状态”（无条件的手段）包括变量的先验。根据文献中的最新发展，我们讨论了模型的扩展，提高了建模方法的灵活性。这些扩展包括一个分层收缩前的稳态参数，并使用随机波动模型异方差。我们把建议的模型用于预测评估使用美国数据组成的10个月和3个季度的变量。结果表明，预测能力通常受益于使用混合频率的数据，并可以获得改善的月度和季度变量。我们还发现，稳态先验一般提高了预测的准确性，并通过随机波动率的异方差会计通常提供了额外的改进，虽然不是所有变量。
英文摘要：
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.
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