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Forecasting the Confirmed COVID‐19 Cases Using Modal Regression

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  • Xin Jing
  • Jin Seo Cho

Abstract

This study utilizes modal regression to forecast the cumulative confirmed COVID‐19 cases in Canada, Japan, South Korea, and the United States. The objective is to improve the accuracy of the forecasts compared to standard mean and median regressions. To evaluate the performance of the forecasts, we conduct simulations and introduce a metric called the coverage quantile function (CQF), which is optimized using modal regression. By applying modal regression to popular time‐series models for COVID‐19 data, we provide empirical evidence that the forecasts generated by the modal regression outperform those produced by the mean and median regressions in terms of the CQF. This finding addresses the limitations of the mean and median regression forecasts.

Suggested Citation

  • Xin Jing & Jin Seo Cho, 2025. "Forecasting the Confirmed COVID‐19 Cases Using Modal Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1578-1601, July.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:4:p:1578-1601
    DOI: 10.1002/for.3261
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    Cited by:

    1. Xin Jing & Jin Seo Cho, 2025. "Quantile ARDL Estimation of the Relationship between the Confirmed COVID-19 Cases and Deaths in the U.S," Working papers 2025rwp-247, Yonsei University, Yonsei Economics Research Institute.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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