Neural network versus econometric models in forecasting inflation

Artificial neural network modelling has recently attracted much attention a s a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional metho ds tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) models with the traditional econometric app roaches to forecasting the inflation rate. Of the traditional econometric m odels we use a structural reduced-form model, an ARIMA model, a vector auto regressive model, and a Bayesian vector autoregression model. We compare ea ch econometric model with a hybrid BPN model which uses the same set of var iables. Dynamic forecasts are compared for three different horizons: one, t hree and twelve months ahead. Root mean squared errors and mean absolute er rors are used to compare quality of forecasts. The results show the hybrid BPN models are able to forecast as well as all the traditional econometric methods, and to outperform them in some cases. Copyright (C) 2000 John Wile y & Sons, Ltd.