Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination Schulz, Felix Setzer, Thomas Balla, Nathalie 2021-12-24T17:36:57Z 2021-12-24T17:36:57Z 2022-01-04
dc.description.abstract Forecast combination is an established methodology to improve forecast accuracy. The primary questions in the current literature are how many and which forecasts to include (selection) and how to weight the selected forecasts (weighting). Although integrating both tasks seems appealing, we are only aware of a few data analytical models that integrate both tasks. We introduce Linear Hybrid Shrinkage (LHS), a novel method that uses information criteria from statistical learning theory to select forecasters and then shrinks the selection from their in-sample optimal weights linearly towards equality, while shrinking the non-selected forecasts towards zero. Simulation results show conditions (scenarios) where LHS leads to higher accuracy than LASSO-based Shrinkage, Linear Shrinkage of in-sample optimal weights, and a simple averaging of forecasts.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.267
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Soft Computing: Theory Innovations and Problem Solving Benefits
dc.subject forecast combination
dc.subject forecast selection
dc.subject lasso
dc.subject shrinkage
dc.subject variable importance
dc.title Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination
dc.type.dcmi text
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