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An econometric panel data model of the COVID-19 pandemic

Author

Listed:
  • Antoine Djogbenou

    (Unknown)

  • Christian Gourieroux

    (Unknown)

  • Joann Jasiak

    (Unknown)

  • Paul Rilstone

    (Unknown)

Abstract

New flexible-form and semi-parametric autoregressive non-linear count models for panel data are developed to analyse the spread and containment of the COVID-19 pandemic. The models are based on a discrete time form of the SIR model. These methods lead naturally to estimators of the infection process and daily reproduction numbers by jurisdiction. Two semi-parametric versions of the reproduction numbers are developed corresponding to currently popular parametric estimators. The estimators are applied to a large international data set to estimate these parameters for 221 jurisdictions at both national and subnational levels. Â Â JEL classification numbers: C14, C23, I18.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone, 2022. "An econometric panel data model of the COVID-19 pandemic," Post-Print hal-03641783, HAL.
    • Handle: RePEc:hal:journl:hal-03641783
    DOI: 10.47260/jsem/1113
    as

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    References listed on IDEAS

    as
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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