Inflation Forecasting Post‐COVID‐19: Evidence From Germany

ABSTRACT I compare the forecasting performance of a wide range of univariate and multivariate forecasting models for both headline and core inflation in Germany. The analysis is conducted using a full sample containing quarterly data from 1995 to 2024, which includes the COVID‐19 pandemic and subsequent inflation surge, and a shortened sample spanning the years 1995–2019, a period of low and stable inflation. I firstly find evidence to support a recurrent finding in the literature that univariate models are hard to beat but show that this holds particularly for single‐indicator models, at times of stable inflation. Furthermore, upon testing 34 indicators for German inflation in both single‐indicator and combination (i.e., factor, pooling, and BVAR) models, I find that the performance of multivariate models is improved by (i) including the years 2020–2024 in which inflation was high and volatile and (ii) by combining information contained within the indicators. Specifically, I find that the multivariate models improve upon the univariate benchmark by up to 20% in the full sample, providing evidence for the performance of forecasting models to vary with the economic environment. The analysis also sheds light on which indicators are particularly useful for forecasting inflation in Germany. Additionally, it appears that multivariate models provide a greater forecasting gain over the univariate benchmark for headline, rather than core, inflation. Finally, a short case study of the inflation pickup following the COVID‐19 pandemic reveals that the multivariate models, particularly the factor, BVAR, and price expectations single‐indicator models, most accurately forecasted inflation at this time.