Forecasting inflation in the euro area: countries matter!
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Forecasting inflation in the euro area: countries matter! Angela Capolongo1,2
· Claudia Pacella1,3
Received: 23 October 2019 / Accepted: 8 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, cross-country dynamic interactions, and country-specific variables. The model provides good forecasting accuracy with respect to the popular benchmarks used in the literature. We perform a step-by-step analysis to shed light on which layer of information is more crucial for accurately forecasting medium-run euro area inflation. Our empirical analysis reveals the importance of including the key drivers of inflation and taking into account the multi-country dimension of the euro area. The results show that the complete model performs better overall in forecasting inflation excluding energy and unprocessed food over the medium term. We use the model to establish stylized facts on the euro area and cross-country heterogeneity over the business cycle. Keywords Inflation · Forecasting · Bayesian estimation · Multi-country model · Euro area
1 Introduction The primary objective of the European Central Bank is to maintain price stability in the euro area as a whole. This general goal has been further specified in terms of keeping the year-on-year increase in the euro area Harmonized Index of Consumer Prices (HICP) below, but close to 2% over the medium term. Given this objective, a timely assessment of the economic drivers and the most likely outlook for inflation are a fundamental input for monetary policy. However, as reviewed by Faust and Wright (2013), while a large number of models have been proposed to forecast inflation,
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Angela Capolongo [email protected]
1
ECARES, Université Libre de Bruxelles, Brussels, Belgium
2
CEPS, Brussels, Belgium
3
Bank of Italy, Rome, Italy
123
A. Capolongo, C. Pacella
interpreting the inflation dynamics and providing an informed view on the inflation outlook has always been a challenging exercise. For the US, Atkeson and Ohanian (2001) show that it is difficult to outperform very simple models as the random walk, while Stock and Watson (2007) find that the inflation process is well represented by a univariate unobserved component timevarying trend-cycle model. Similarly, for the euro area, Fischer et al. (2009) highlight the good inflation forecasting performance of the random walk model, and Diron and Mojon (2005) provide evidence that the central bank’s objective targets yield more accurate forecasts than most inflation forecast models. The aim of this paper is to contribute to the literature on forecasting inflation in the euro area at the short- and medium-term horizon. We consider forecasts for both the headline HICP and the HICP excluding energy and unprocessed food, which is often referred to as a measure of core inflation, meant to capture the most persistent component of consumer prices. Specifically, we address the questi
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