Forecasting inflation with ANN models

Abstract

In this research I investigate the alternative methodology of training artificial neural networks models with the early stopping procedure and I analyze their outcomes in terms of accuracy when forecasting monthly Paraguayan inflation time series. The results show that despite of neural network modelling being a competitive alternative to classical linear modelling it doesn‟t improve the overall forecast performance of best ARMA specifications selected through common in-sample estimation procedures, in a set of four control subsamples of 24 months each, ranging from 2002:04 to 2010:04. However, it is also a remarkable feature of all the checks performed in this research, that artificial neural network models outperform ARMA specifications in 24-steps-ahead horizon forecasts in all the subsamples of control.