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Depth-Weighted Forecast Combination: Application to COVID-19 Cases

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Abstract
We develop a novel forecast combination based on the order statistics of individual predictability when many forecasts are available. To this end, we define the notion of forecast depth, which measures the size of forecast errors during the training period and provides a ranking among different forecast models. The forecast combination is in the form of a depth-weighted trimmed mean, where the group of models with the worst forecasting performance during the training period is dropped. We derive the limiting distribution of the depth-weighted forecast combination, based on which we can readily construct forecast confidence intervals. Using this novel forecast combination, we forecast the national level of new COVID-19 cases in the U.S. and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention. We find that the depth-weighted forecast combination yields more accurate predictions compared with other forecast combinations.

Suggested Citation

  • Yoonseok Lee & Donggyu Sul, 2021. "Depth-Weighted Forecast Combination: Application to COVID-19 Cases," Center for Policy Research Working Papers 238, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:238
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    References listed on IDEAS

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    1. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
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    More about this item

    Keywords

    Forecast Combination; Forecast depth; Depth-weighted trimmed mean; COVID-19;
    All these keywords.

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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