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

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

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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.

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Article provided by Taylor and Francis Journals in its journal Applied Economics.

Volume (Year): 37 (2005)
Issue (Month): 6 (April)
Pages: 665-680
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Handle: RePEc:taf:applec:v:37:y:2005:i:6:p:665-680

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. J. M. Binner & R. K. Bissoondeeal & A. W. Mullineux, 2005. "A composite leading indicator of the inflation cycle for the Euro area," Applied Economics, Taylor and Francis Journals, vol. 37(11), pages 1257-1266, June. [Downloadable!] (restricted)
  2. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer, vol. 28(1), pages 71-88, August. [Downloadable!] (restricted)
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