Forecasting inflation with the hedged random forest

Elliot Beck and Michael Wolf

No 2025-07, Working Papers from Swiss National Bank

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, the random forest stands out as a particularly effective approach that consistently outperforms traditional benchmark models in empirical studies. Building on this foundation, this paper adapts the hedged random forest (HRF) framework of Beck et al. (2024) 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. We develop 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. An extensive empirical analysis demonstrates that the proposed approach consistently outperforms the standard random forest.

Keywords: Exponentially weighted moving average; Linear shrinkage; Machine learning (search for similar items in EconPapers)
JEL-codes: C21 C31 C53 E47 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2025
New Economics Papers: this item is included in nep-ets, nep-for and nep-mon
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