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Pandemic Intensity Estimation using Dynamic Factor Modeling

Author

Listed:
  • Cooke Aaron

    (U.S. Department of the Treasury, Washington D.C., USA. The views represented are those of the author and not necessarily those of the U.S. Department of the Treasury or the United States Government)

  • Vivian John

    (Independent Researcher, San Francisco, USA)

Abstract

Individual and policy reactions to the coronavirus pandemic had disparate impacts on viral transmission and were heterogeneous in their influence on economic activity and personal outcomes (Kerpen, Phil, Stephen Moore, and Casey B. Mulligan. 2022. A Final Report Card on the States’ Response to Covid-19. Working Paper 29928. National Bureau of Economic Research). Pandemic researchers struggle with choosing from multiple measurements of disease intensity. This paper is the first to suggest using a restricted data-rich dynamic factor model, generated from a variety of economic and pandemic data series to provide a comprehensive measurement of disease intensity. We use this approach to evaluate vaccination efficacy. We also provide future researchers with an open-source Python package that can run a restricted dynamic factor model with bespoke data input. By using the information generated by this specification, policy makers can choose how to respond to future pandemics with a deeper understanding of the costs and benefits of their choices. This paper concentrates on the United States, and exploits variation between U.S. states, but this approach is generalizable for any populations with similar data availability.

Suggested Citation

  • Cooke Aaron & Vivian John, 2025. "Pandemic Intensity Estimation using Dynamic Factor Modeling," Statistics, Politics and Policy, De Gruyter, vol. 16(1), pages 37-61.
    • Handle: RePEc:bpj:statpp:v:16:y:2025:i:1:p:37-61:n:1003
    DOI: 10.1515/spp-2024-0042
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    References listed on IDEAS

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    1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    2. Geweke, John, 1984. "Inference and causality in economic time series models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 19, pages 1101-1144, Elsevier.
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    Cited by:

    1. Wagschal Uwe & Schleehauf Ronald & Reinbold Judith, 2025. "Editors’ Note," Statistics, Politics and Policy, De Gruyter, vol. 16(1), pages 1-4.

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    Keywords

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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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