Forecasting inflation in the euro area: countries matter!

Abstract

We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, crosscountry 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.

Predicting inflation component drivers in Nigeria: a stacked ensemble approach

“…Existing studies have adopted several short term forecasting models to predict inflation trend. These models vary from multivariate (Kelikume and Salami 2014 ; Gaomab 1998 ; Capolongo and Pacella 2020 ) to univariate structural models (Akdogan et al. 2012 ; Junttila and Korhonen 2011 ; Pufnik 2006 ).…”