Forecasting Using Random Subspace Methods

Tom Boot () and Didier Nibbering
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Tom Boot: Erasmus University Rotterdam, the Netherlands

No 16-073/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: Random subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. Forecasts are constructed from random subsets of predictors or randomly weighted predictors. We provide a theoretical justification for these strategies by deriving bounds on their asymptotic mean squared forecast error, which are highly informative on the scenarios where the methods work well. Monte Carlo simulations confirm the theoretical findings and show improvements in predictive accuracy relative to widely used benchmarks. The predictive accuracy on monthly macroeconomic FRED-MD data increases substantially, with random subspace methods outperforming all competing methods for at least 66% of the series.

Keywords: dimension reduction; forecasting; random subspace (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 C55 (search for similar items in EconPapers)
Date: 2016-09-06, Revised 2017-08-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Related works:
Journal Article: Forecasting using random subspace methods (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20160073

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