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. © 2005 Taylor & Francis Group Ltd.

Publication DOI: https://doi.org/10.1080/0003684052000343679
Divisions: College of Business and Social Sciences > Aston Business School > Economics, Finance & Entrepreneurship
Additional Information: This is an electronic version of an article published in Binner, Jane M.; Bissoondeeal, Rakesh; Elger, Thomas; Gazely, Alicia M. and Mullineux, Andrew W. (2005) A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia. Applied Economics, 37 (6). pp. 665-680. ISSN 0003-6846. Applied Economics is available online at: http://www.tandfonline.com/10.1080/0003684052000343679
Uncontrolled Keywords: linear models,limitations,nonlinearities in the data,relative Euro inflation forecasting performance,Economics and Econometrics
Publication ISSN: 1466-4283
Last Modified: 13 Mar 2024 08:08
Date Deposited: 06 Oct 2009 15:14
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://www.tand ... 684052000343679 (Publisher URL)
PURE Output Type: Article
Published Date: 2005-04-10
Authors: Binner, Jane M.
Bissoondeeal, Rakesh (ORCID Profile 0000-0003-3950-0507)
Elger, Thomas
Gazely, Alicia M.
Mullineux, Andrew W.



Version: Accepted Version

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