Density forecasts of inflation: A quantile regression forest approach - Michele Lenza; Inès Moutachaker; Joan Paredes (European Central Bank (ECB), 2023)
Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large set of determinants, is competitive with state-of-the-art linear benchmarks and judgemental survey forecasts. The median forecasts of the quantile regression forest are very collinear with the ECB point inflation forecasts, displaying similar deviations from "linearity". Given that the ECB modelling toolbox is overwhelmingly linear, this finding suggests that the expert judgement embedded in the ECB forecast may be characterized by some mild non-linearity.
Keywords: C52; C53; E31; E37; 330; Inflation; Non-linearity; Quantile Regression Forest