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.
# 16-073/III (2016-09-06; 2017-08-11)
- Tom Boot, Erasmus University Rotterdam, the Netherlands; Didier Nibbering, Erasmus University Rotterdam, the Netherlands
- dimension reduction, forecasting, random subspace
- JEL codes:
- C32, C38, C53, C55