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<p>Accurate inflation forecasting is essential for economic policy, financial markets, and broader societal stability. In recent years, machine learning methods, particularly the random forest, have shown strong potential for improving forecasting accuracy, often outperforming traditional benchmarks. Building on this success, our paper adapts the hedged random forest (HRF) of Beck et al. (The hedged random forest, 2024) to the task of forecasting inflation. Unlike the standard random forest, the HRF assigns non-equal, and potentially negative, weights to individual trees to enhance forecasting performance. We develop customized estimators for the HRF’s two key inputs: the mean and covariance matrix of the vector of forecast errors corresponding to individual trees. An extensive empirical analysis demonstrates that our approach consistently outperforms the standard random forest.</p>

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Forecasting inflation with the hedged random forest

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Elliot Beck,
  • Michael Wolf

摘要

Accurate inflation forecasting is essential for economic policy, financial markets, and broader societal stability. In recent years, machine learning methods, particularly the random forest, have shown strong potential for improving forecasting accuracy, often outperforming traditional benchmarks. Building on this success, our paper adapts the hedged random forest (HRF) of Beck et al. (The hedged random forest, 2024) to the task of forecasting inflation. Unlike the standard random forest, the HRF assigns non-equal, and potentially negative, weights to individual trees to enhance forecasting performance. We develop customized estimators for the HRF’s two key inputs: the mean and covariance matrix of the vector of forecast errors corresponding to individual trees. An extensive empirical analysis demonstrates that our approach consistently outperforms the standard random forest.