 A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro DivisiaJane Binner, Rakesh Bissoondeeal, Thomas Elger, Alicia Gazely and Andrew Mullineux Applied Economics, 2005, vol. 37, issue 6, 665-680 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. Date: 2005 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:37:y:2005:i:6:p:665-680 Ordering information: This journal article can be ordered from DOI: 10.1080/0003684052000343679 Access Statistics for this article Applied Economics is currently edited by Anita Phillips More articles in Applied Economics from Taylor & Francis Journals |
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