英文文献：A mixed-frequency Bayesian vector autoregression with a steady-state prior-一种具有稳态先验的混合频率贝叶斯向量自回归
英文文献作者：Sebastian Ankargren,M?ns Unosson,Yukai Yang
英文文献摘要：
We consider a Bayesian vector autoregressive (VAR) model allowing for an explicit prior specification for the included variables' "steady states" (unconditional means) for data measured at different frequencies. We propose a Gibbs sampler to sample from the posterior distribution derived from a normal prior for the steady state and a normal-inverse-Wishart prior for the dynamics and error covariance. Moreover, we suggest a numerical algorithm for computing the marginal data density that is useful for finding appropriate values for the necessary hyperparameters. We evaluate the proposed model by applying it to a real-time data set where we forecast Swedish GDP growth. The results indicate that the inclusion of high-frequency data improves the accuracy of low-frequency forecasts, in particular for shorter time horizons. The proposed model thus facilitates a simple and helpful way of incorporating information about the long run through the steady-state prior as well as about the near future through its ability to cope with mixed frequencies of the data.

我们考虑了一个贝叶斯向量自回归(VAR)模型，该模型允许对不同频率测量的数据的包含变量的“稳定状态”(无条件的方法)进行明确的预先说明。我们提出了一个吉布斯采样器，用于从稳态的正态先验和动态和误差协方差的正态反wishart先验导出的后验分布进行采样。此外，我们提出了一种计算边缘数据密度的数值算法，这对于寻找必要的超参数的适当值是有用的。我们通过将该模型应用于预测瑞典GDP增长的实时数据集来评估该模型。结果表明，高频数据的加入提高了低频预测的准确性，特别是在较短的时间范围内。因此，所提出的模型促进了一种简单和有益的方式，通过稳定状态之前的长期运行以及通过处理数据的混合频率的能力，关于近期的未来的信息。