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

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.

Citation

Lee, Y. and Sul, D. (2023), "Depth-weighted Forecast Combination: Application to COVID-19 Cases", Chang, Y., Lee, S. and Miller, J.I. (Ed.) Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications (Advances in Econometrics, Vol. 45B), Emerald Publishing Limited, Leeds, pp. 235-260. https://doi.org/10.1108/S0731-90532023000045B011