AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights

Filip Blaha, Jan Botka, Josef Sveda and Ales Michl

Working Papers from Czech National Bank, Research and Statistics Department

Abstract: We construct a quantile regression forest for inflation forecasting in the Czech Republic, inspired by growing literature on the use of Machine Learning in macroeconomics and finance. We contribute to the literature by implementing an optimisation scheme with time-varying weights that incorporates information from the entire distribution to form the point forecast. By dynamically reflecting the distribution of future inflation paths, our framework outperforms both standard mean and median point forecasts and delivers gains relative to conventional linear benchmark models. We also forecast individual inflation subcomponents that enable us to disentangle the drivers of future inflation and its risks. Furthermore, we integrate the Shapley-value decomposition to enhance the interpretability of our results and adjust the model's predictors for a small open economy.

Keywords: Czech Republic; forecasting; inflation; machine learning; quantile regression forest; small open economy; time varying weights (search for similar items in EconPapers)
JEL-codes: C53 C55 E31 E37 E52 (search for similar items in EconPapers)
Date: 2026-04
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