一种具有稳态的柔性混频向量自回归 先验

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摘要翻译：
提出了一种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，并允许对所包含变量的“稳态”（无条件均值）进行显式先验。基于文献中的最新进展，我们讨论了模型的扩展，以提高建模方法的灵活性。这些扩展包括稳态参数的分层收缩先验，以及使用随机波动率来模型异方差性。我们将所提出的模型应用于由10个月和3个季度变量组成的美国数据的预测评价。结果表明，使用混频数据可以提高预测能力，并且对月度和季度变量都可以获得改进。我们还发现，稳态先验通常会提高预测的准确性，通过随机波动来考虑异方差通常会提供额外的改进，尽管不是对所有变量都有改进。
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英文标题：
《A Flexible Mixed-Frequency Vector Autoregression with a Steady-State
  Prior》
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作者：
Sebastian Ankargren and M{\aa}ns Unosson and Yukai Yang
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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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PDF链接：
https://arxiv.org/pdf/1911.09151

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关键词：向量自回归 自回归 econometrics Improvements Hierarchical use 向量 steady stochastic 稳态