 Flexible global forecast combinationsRyan Thompson, Yilin Qian and Andrey Vasnev Omega, 2024, vol. 126, issue C Abstract: Forecast combination – the aggregation of individual forecasts from multiple experts or models – is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations. Keywords: Forecast combination; Local forecasting; Global forecasting; Multi-task learning; European Central Bank; Survey of Professional Forecasters (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:126:y:2024:i:c:s0305048324000409 Ordering information: This journal article can be ordered from DOI: 10.1016/j.omega.2024.103073 Access Statistics for this article Omega is currently edited by B. Lev More articles in Omega from Elsevier |
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