Department of Economics and Business Economics

Global Inflation Forecasting: Benefits from Machine Learning Methods

Research output: Working paper/Preprint Working paperResearch

This paper considers inflation forecasting for a vast panel of countries. We combine the information from common factors driving global inflation as well as country-specific inflation in order to build a set of different models. We also rely on new advances in the Machine Learning literature. We show that random forests and neural networks are very competitive models, and their superiority, although stable across most of the time period considered, increases during recessions. We also show that it is easier to forecast countries with more developed economies. The forecasting gains seem to be partially explained by the degree of trade openness.
Original languageEnglish
Number of pages74
Publication statusPublished - Jun 2022

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