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Title: Global inflation forecasting: Benefits from machine learning methods Authors:  Tobias Soussi - Aarhus University (Denmark) [presenting]
Marcelo Medeiros - PUC-Rio (Brazil)
Erik Christian Montes Schutte - Aarhus University (Denmark)
Abstract: Inflation forecasting for a vast panel of countries is considered. We combine the information from common factors driving global inflation and country-specific inflation 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 and the volatility of inflation within a year.