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Forecasting inflation in the euro area: countries matter!

Angela Capolongo and Claudia Pacella ()

No 1224, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

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 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, while a model based only on aggregate euro area variables works better for headline inflation.

Keywords: inflation; forecasting; euro area; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C32 C53 E31 E37 (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-cba, nep-eec, nep-ets, nep-for, nep-mac and nep-mon
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