Aggregating Judgmental Demand Forecasts in Environments with Structural Breaks

We study the effectiveness of judgmental forecast aggregation in environments in which structural breaks cause sudden shifts in the mean demand. In such scenarios, judgmental forecasts are often biased, depending on whether demand shifts upward or downward. We demonstrate that asymmetric trimming of judgmental forecasts can increase the likelihood of bracketing in the presence of structural breaks, whereas symmetric trimming performs better in stable environments. Given that the timing and directionality of structural breaks are typically unpredictable, we propose two forecast combination methods that select the appropriate trimming rule by dynamically adapting to structural changes observed. The break perception method draws on subjective judgments regarding the occurrence of a structural break to determine which trimming rule should be chosen. The past performance method, on the other hand, relies on historical forecast errors of various trimming rules as a criterion for selection. We test their performance using both artificially generated data and real-world data on the daily U.S. COVID-19 death toll. Our results highlight that these combination methods consistently outperform other aggregation approaches. We discuss the implications of our findings for managerial practice. Funding: This research is funded by a KAIST Faculty Research Grant, the Ministerio de Ciencia e Innovación España [Grants 10.13039/501100011033, HACOM-PID2022-138667OB-I00, and HDL-HS-280218], and the Institute of Information & Communications Technology Planning & Evaluation-Global Data-X Leader HRD program grant funded by the Korea government (MSIT) [Grant IITP-RS-2024-00440626]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2025.0417 .