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

Yoonseok Lee and Donggyu Sul ()

A chapter in Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, 2023, vol. 45B, pp 235-260 from Emerald Group Publishing Limited

Abstract: The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.

Keywords: Forecast depth; forecast combination; panel forecast; prediction interval; robust forecast; COVID-19; C32; C33; C53 (search for similar items in EconPapers)
Date: 2023
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Working Paper: Depth-Weighted Forecast Combination: Application to COVID-19 Cases (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532023000045b011

DOI: 10.1108/S0731-90532023000045B011

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