Paper by alumni Tom Boot and Didier Nibbering published in the Journal of Econometrics
The paper ‘Forecasting Using Random Subspace Methods' by alumni Tom Boot (University of Groningen) and Didier Nibbering (Monash University, Australia) has been published in the Journal of Econometrics. Read full paper here.
Random subspace methods are a new approach to obtain accurate forecasts in high-dimensional regression settings. Forecasts are constructed by averaging over forecasts from many submodels generated by random selection or random Gaussian weighting of predictors. This paper derives upper bounds on the asymptotic mean squared forecast error of these strategies, which show that the methods are particularly suitable for macroeconomic forecasting. An empirical application to the FRED-MD data confirms the theoretical findings, and shows random subspace methods to outperform competing methods on key macroeconomic indicators.