Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/304455 
Authors: 
Year of Publication: 
2024
Series/Report no.: 
IWH Discussion Papers No. 21/2024
Publisher: 
Halle Institute for Economic Research (IWH), Halle (Saale)
Abstract: 
This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast's contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.
Subjects: 
forecast combination
forecast combination puzzle
forecasting
random subset
Shapley value decomposition
JEL: 
C22
C45
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.