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Tobias Skipper Soussi

Global Inflation: Implications for forecasting and monetary policy

Publikation: Working paper/Preprint › Working paper › Forskning

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This paper considers inflation forecasting for a vast panel of countries. We combine the information from common factors driving global and country-specific inflation to build 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 inflation volatility within a year. Our results have two significant implications for monetary policy. First, our forecasts can serve as inflation expectations for countries where survey data are unavailable. Second, we shed some light on the links between inflation from different countries, facilitating the study of the transmission of monetary shocks.
OriginalsprogEngelsk
Antal sider83
StatusUdgivet - jun. 2022

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