This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the state-space literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.

翻译：本文概括了多维依赖数据的动态要素模型。 在此过程中,它开发了一种可解释的技术,以研究复杂的信息来源,从对不同数目的答卷人进行重复调查到卫星图像板等。我们将结果专门用于将美国家庭微观经济数据与宏观经济总量相结合的模型。这产生了一个强大的工具,能够为个体家庭产生本地化的预测、反事实和冲动反应功能,其中考虑到州空间文献中描述的传统时间序列复杂性。 该模型也与决策者日益重视实时经济分析相匹配,因为决策者能够在线处理观测,同时处理缺失的值和无同步的数据发布。