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Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination

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Schulz, Felix ; Setzer, Thomas ; Balla, Nathalie:
Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination.
2022
Veranstaltung: HICSS 2022 : Hawaii International Conference on System Sciences, 04.01.2022 - 07.01.2022, Hawaii, USA.
(Veranstaltungsbeitrag: Kongress/Konferenz/Symposium/Tagung, Paper)

Kurzfassung/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

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Publikationsform:Veranstaltungsbeitrag (unveröffentlicht): Kongress/Konferenz/Symposium/Tagung, Paper
Sprache des Eintrags:Englisch
Institutionen der Universität:Wirtschaftswissenschaftliche Fakultät > Betriebswirtschaftslehre > ABWL und Wirtschaftsinformatik
Titel an der KU entstanden:Ja
KU.edoc-ID:28946
Eingestellt am: 25. Nov 2021 08:10
Letzte Änderung: 25. Nov 2021 08:10
URL zu dieser Anzeige: https://edoc.ku.de/id/eprint/28946/
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