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

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.