SNB paper shows 5–6% boost in inflation forecasting
Hedged random forest method “consistently outperforms” unhedged approach
Swiss National Bank (SNB) researchers say they have managed to improve upon the already popular “random forest” machine learning-based inflation prediction model.
In their working paper, published on June 4, Eliot Beck and Michael Wolf show that by assigning “non-equal (and even negative weights) of the individual trees” to random forest models, inflation forecasts can be improved by around 5% in terms of the root mean-squared error (RMSE) and 6% in terms of the mean absolute error (MAE).
Beck and
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