Forecasting the Confirmed COVID-19 Cases Using Modal Regression

Xin Jing and Jin Seo Cho ()
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Xin Jing: Yonsei University

No 2023rwp-217, Working papers from Yonsei University, Yonsei Economics Research Institute

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 COVID19 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.

Keywords: Forecasting COVID-19 cases; Modal regression; Conditional mode; MEM algorithm; Density estimation. (search for similar items in EconPapers)
JEL-codes: C22 C53 I18 (search for similar items in EconPapers)
Pages: 42pages
Date: 2023-06
New Economics Papers: this item is included in nep-ets and nep-for
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