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We investigate whether the use of statistical learning techniques and big data can enhance the accuracy of inflation forecasts. We make use of a large dataset for the disaggregated prices of consumption goods and services, which we partially reconstruct, and a large suite of different statistical learning and traditional time-series models. The results suggest that the statistical learning models are able to compete with most benchmarks over medium to longer horizons, despite the fact that we only have a relatively small sample of available data. This may imply that the ability of statistical learning models to explain nonlinear relationships, or as an alternative, restrict the set of predictors to relevant information, is of importance. These characteristics of the statistical learning models may be particularly useful during periods of crisis, when deviations from the steady state are more persistent. We find that the accuracy of the central bank's near-term inflation forecasts compares favourably with those of other models, while the inclusion of off-model information, such as electricity tariff adjustments and other sources of within-month data, provides these models with a competitive advantage. Lastly, we also investigate the relative performance of the different models as we experienced the effects of the recent pandemic and identify the most important contributors to future inflationary pressure.The online version contains supplementary material available at 10.1007/s00181-022-02329-y. |
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