IDEAS home Printed from https://ideas.repec.org/a/bpj/statpp/v16y2025i1p37-61n1003.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/spp-2024-0042
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/spp-2024-0042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.
    2. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    3. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    4. Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario & Altissimo, Filippo & Cristadoro, Riccardo & Veronese, Giovanni & Bassanetti, Antonio, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    5. Matteo Barigozzi & Angelo Cuzzola & Marco Grazzi & Daniele Moschella, 2025. "Factoring in the Micro: A Transaction‐Level Dynamic Factor Approach to the Decomposition of Export Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(1), pages 155-184, February.
    6. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    7. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    8. Marek Jarociński & Bartosz Maćkowiak, 2017. "Granger Causal Priority and Choice of Variables in Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 319-329, May.
    9. Hansson, Jesper & Jansson, Per & Löf, Mårten, 2003. "Business Survey Data: Do They Help in Forecasting the Macro Economy?," Working Papers 84, National Institute of Economic Research.
    10. Marc Hallin & Siegfried Hörmann & Marco Lippi, 2018. "Optimal dimension reduction for high-dimensional and functional time series," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 385-398, July.
    11. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    12. Marco Causi & Andrea Baldini, 2018. "Determinants Of Loan And Bad Loan Dynamics: Evidence From Italy," Departmental Working Papers of Economics - University 'Roma Tre' o232, Department of Economics - University Roma Tre.
    13. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    14. Mr. Emil Stavrev, 2006. "Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy," IMF Working Papers 2006/197, International Monetary Fund.
    15. Poncela, Pilar & Ruiz Ortega, Esther, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    17. Marin, Dalia, 1992. "Is the Export-Led.Growth Hypothesis Valid for Industrialized Countries?," The Review of Economics and Statistics, MIT Press, vol. 74(4), pages 678-688, November.
    18. Molero-González, L. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & García-Medina, A., 2023. "Market Beta is not dead: An approach from Random Matrix Theory," Finance Research Letters, Elsevier, vol. 55(PA).
    19. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    20. Davide Brignone & Alessandro Franconi & Marco Mazzali, 2023. "Robust Impulse Responses using External Instruments: the Role of Information," Papers 2307.06145, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:statpp:v:16:y:2025:i:1:p:37-61:n:1003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.