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 new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured 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. It is also investigated 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 non-linear 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 non-linear framework.
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Document Type: Research Article
Strategic Management Group, Aston Business School, Aston University, Birmingham, B4 7ET, UK
Department of Information Management and Systems, Nottingham Business School, Nottingham Trent University, Nottingham, NG1 4BU, UK
Department of Economics, Lund University, P.O. Box 7082, Lund, Sweden
Department of Accounting and Finance, Birmingham Business School, The University of Birmingham, B15 2TT, UK
Publication date: 2005-04-10
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