Bagged neural networks for forecasting Polish (low) inflation

Abstract

Accurate inflation forecasts lie at the heart of effective monetary policy. This paper utilizes a thick modelling approach in order to investigate the quality of the out-of-sample short-term headline inflation forecasts generated by a combination of bagged single hidden-layer feed-forward artificial neural networks. The model’s accuracy rises during the period of consistently falling and persistently low inflation in the emerging economy of Poland, and it statistically outperforms some of the popular benchmarks more frequently, especially at longer horizons. However, dispensing with data preprocessing and bootstrap aggregation compromises its forecasting ability severely. Combining linear and non-linear univariate and multivariate approaches with diverse underlying model assumptions delivers further gains in predictive accuracy and statistically outperforms a panel of benchmarks in a number of cases. While the vague interpretability of the model poses a considerable hurdle for policy makers, its inclusion in the forecasting toolbox should increase the accuracy of the ensemble of models, especially in periods of structural change.