Global Inflation: Implications for forecasting and monetary policy

83 Pages Posted: 30 Jun 2022 Last revised: 17 Apr 2023

See all articles by Marcelo C. Medeiros

Marcelo C. Medeiros

The University of Illinois at Urbana-Champaign

Erik Christian Montes Schütte

Aarhus University; Aarhus University - CREATES; DFI

Tobias Skipper Soussi

Aarhus University - School of Business and Social Sciences; Aarhus University - CREATES

Date Written: June 24, 2022

Abstract

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.

Keywords: global inflation, inflation forecasting, machine learning, random forests, neural networks, shrinkage

JEL Classification: C53, C55, E31, E37, F15

Suggested Citation

Cunha Medeiros, Marcelo and Schütte, Erik Christian Montes and Schütte, Erik Christian Montes and Soussi, Tobias Skipper, Global Inflation: Implications for forecasting and monetary policy (June 24, 2022). Available at SSRN: https://ssrn.com/abstract=4145665 or http://dx.doi.org/10.2139/ssrn.4145665

Marcelo Cunha Medeiros

The University of Illinois at Urbana-Champaign ( email )

1407 West Gregory Drive
Urbana, IL 61801
United States

Erik Christian Montes Schütte (Contact Author)

Aarhus University ( email )

Nordre Ringgade 1
DK-8000 Aarhus C, 8000
Denmark

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

HOME PAGE: http://sites.google.com/view/christian-montes-schutte/home

DFI ( email )

Tobias Skipper Soussi

Aarhus University - School of Business and Social Sciences ( email )

Nordre Ringgade 1
Aarhus C, DK-8000
Denmark

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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