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

Jane M. Binner, Rakesh K. Bissoondeeal, Thomas Elger, Alicia M. Gazely, Andrew W. Mullineux

Research output: Contribution to journalArticlepeer-review

42 Citations (SciVal)

Abstract

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.
Original languageEnglish
Pages (from-to)665-680
JournalApplied Economics
Volume37
Issue number6
DOIs
Publication statusPublished - 2005

Subject classification (UKÄ)

  • Business Administration

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