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Abstract
Assessing the risks of having either very low or very high inflation is crucial for policymakers, businesses, and householders. In a globalised world, these risks are increasingly determined by international conditions. In this paper, we empirically analyse the impact of international inflation factors on forecasting monthly domestic inflation risks in a large number of economies observed worldwide from 1999 to 2022. Risk forecasts are obtained using factor-augmented quantile regressions estimated with international factors extracted from a multi-level dynamic factor model with overlapping blocks of inflations corresponding to economies grouped either in a given geographical region or according to their development level. We conclude that in a large number of countries, international factors are relevant to explain the right tail of the distribution of inflation, and consequently they are more relevant for the risk related to high inflation than for low inflation. The role of international factors is stronger in developed European countries, while the inflation risks of low-income developing countries are hardly affected by international conditions, and the results for middle-income countries are mixed. We also show that the predictive power of international factors has increased in the most recent years of high inflation.
Short Bio
Esther Ruiz got her PhD in Econometrics from the London School of Economics in 1992. She is Full Professor of Universidad Carlos III de Madrid since 2008 and Co-Editor of the International Journal of Forecasting since 2009. She is Fellow of the International Institute of Forecasters. She has been leader researcher of several competitive projects, with more than 60 publications in leading academic journals. Some of her works have been reproduced in specialist monographies. Her main research interests focus on the methodology and empirical implementation of time series econometrics with emphasis on modelling economic uncertainty. Her main contributions are related with modelling stochastic volatility, economic forecasting, and using Dynamic factor models to summarize the information in large sets of variables.
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