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arXiv · 2301.12499

Multidimensional dynamic factor models

Matteo Barigozzi·Filippo Pellegrino
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Abstract

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.

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Matteo Barigozzi, Filippo Pellegrino. 2023-01-29. Multidimensional dynamic factor models. https://arxiv.org/abs/2301.12499

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