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How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters

Author

Listed:
  • Paul Ho
  • Thomas A. Lubik
  • Christian Matthes

Abstract

This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

Suggested Citation

  • Paul Ho & Thomas A. Lubik & Christian Matthes, 2020. "How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters," Working Paper 20-10, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:88807
    DOI: 10.21144/wp20-10
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    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling > Statistical Modelling

    More about this item

    Keywords

    Bayesian Estimation; Panel; Time-Varying Parameters;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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