A0274
Title: Forecasting inflation with the hedged random forest
Authors: Michael Wolf - University of Zurich (Switzerland) [presenting]
Elliot Beck - University of Zurich (Switzerland)
Abstract: Accurately forecasting inflation is critical for economic policy, financial markets, and broader societal stability. In recent years, machine learning methods have shown great potential for improving the accuracy of inflation forecasts; specifically, random forests stand out as a particularly effective approach that consistently outperforms traditional benchmark models in empirical studies. Building on this foundation, the hedged random forest (HRF) framework of a prior study is adapted for the task of forecasting inflation. Unlike the standard random forest, the HRF employs non-equal (and even negative) weights of the individual trees, which are designed to improve forecasting accuracy. Estimators of the HRF's two inputs, the mean and the covariance matrix of the errors corresponding to the individual trees, that are customized for the task at hand, are developed. An extensive empirical analysis demonstrates that the proposed approach consistently outperforms the standard random forest.
