A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper we provide new empirical evidence on the relative Euro inflation forecasting performance of linear and nonlinear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas NN are used as nonlinear forecasting models. We endeavour to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. We also investigate whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that nonlinear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a nonlinear framework.