We develop a novel framework for dynamic modelling of mixed-frequency data using Bayesian predictive synthesis. The proposed framework—unlike other mixed-frequency methods—considers data reported at different frequencies as latent factors, in the form of predictive distributions, which are dynamically synthesized and updated to produce coherent forecast distributions. Time-varying biases and interdependencies between data reported at different frequencies are learnt and effectively mapped onto easily interpretable parameters with associated uncertainty. Furthermore, the proposed framework allows for flexible methodological specifications based on policy goals and utility. A macroeconomic study of nowcasting two decades of quarterly US GDP using monthly macroeconomic and financial indicators is presented. In terms of both point and density forecasts, our proposed method significantly outperforms competing methods throughout the quarter, and is competitive with the aggregate Survey of Professional Forecasters. The study further shows that incorporating information during a quarter, and sequentially updating information throughout, markedly improves the performance, while providing timely insights that are useful for decision-making.

中文翻译：

---

用于经济临近预报的混合频率贝叶斯预测合成

我们开发了一种使用贝叶斯预测合成对混合频率数据进行动态建模的新框架。与其他混合频率方法不同，所提出的框架将不同频率报告的数据视为潜在因素，以预测分布的形式，动态合成和更新以产生连贯的预测分布。学习以不同频率报告的数据之间的时变偏差和相互依赖性，并有效地映射到具有相关不确定性的易于解释的参数上。此外，提议的框架允许基于政策目标和效用的灵活方法规范。介绍了一项宏观经济研究，该研究使用月度宏观经济和金融指标来预测 20 年美国季度 GDP。在点预测和密度预测方面，我们提出的方法在整个季度都明显优于竞争方法，并且与专业预测者的总体调查相比具有竞争力。该研究进一步表明，在一个季度内整合信息，并在整个过程中依次更新信息，显着提高了绩效，同时提供了对决策有用的及时洞察。