Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/333424 
Year of Publication: 
2025
Series/Report no.: 
Technical Paper No. 04/2025
Publisher: 
Deutsche Bundesbank, Frankfurt a. M.
Abstract: 
In this paper, we evaluate a set of measures of underlying inflation for Germany using conventional measures, such as core inflation (excluding energy and food items), and alternative measures based on econometric models, machine learning, and micro-price evidence. We compare these measures through detailed in-sample and out-of-sample evaluations. The alternative measures exhibit lower volatility, minimal bias, and superior out-of-sample forecasting accuracy performance. While we find no evidence that any single measure clearly outperforms the others over time, the range of alternatives measures also reflects a somewhat earlier uptick and downturn in light of the recent inflation surge in comparison to traditional ones. In addition, all measures under consideration are highly sensitive to monetary policy shocks.
Subjects: 
Underlying inflation
monetary policy
local projections
machine learning
JEL: 
E31
E37
C22
Document Type: 
Working Paper

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