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Multidimensional dynamic factor models

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  • Matteo Barigozzi
  • Filippo Pellegrino

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.

Suggested Citation

  • Matteo Barigozzi & Filippo Pellegrino, 2023. "Multidimensional dynamic factor models," Papers 2301.12499, arXiv.org.
    • Handle: RePEc:arx:papers:2301.12499
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
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    6. Hasenzagl, Thomas & Pellegrino, Filippo & Reichlin, Lucrezia & Ricco, Giovanni, 2022. "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," CEPR Discussion Papers 17111, C.E.P.R. Discussion Papers.
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    11. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
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    Cited by:

    1. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.

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