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：本文对多维相依数据的动态因子模型进行了推广。通过这一推广，我们发展出一种具有可解释性的技术，可用于研究从受访者数量变化的重复调查到卫星图像面板等多种复杂信息源。我们将研究结果专门应用于联合建模美国住户微观经济数据与宏观经济总量。这产生了一个强大的工具，能够为单个住户生成局部预测、反事实分析和脉冲响应函数，同时兼顾状态空间文献中描绘的传统时间序列复杂性。该模型还与政策制定者对实时经济分析日益增长的关注相兼容，因为它能够在线处理观测数据，同时处理缺失值和异步数据发布。