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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 Binner
  • Rakesh Bissoondeeal
  • Thomas Elger
  • Alicia Gazely
  • Andrew Mullineux

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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File URL: http://www.tandfonline.com/doi/abs/10.1080/0003684052000343679
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Bibliographic Info

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

Volume (Year): 37 (2005)
Issue (Month): 6 ()
Pages: 665-680

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Handle: RePEc:taf:applec:v:37:y:2005:i:6:p:665-680

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Citations

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Cited by:
  1. Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.
  2. A. Malliaris & Mary Malliaris, 2013. "Are oil, gold and the euro inter-related? Time series and neural network analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 1-14, January.
  3. Jane Binner & Rakesh Bissoondeeal & Andrew Mullineux, 2004. "A Composite Leading Indicator of the Inflation Cycle for the Euro Area," Money Macro and Finance (MMF) Research Group Conference 2004 24, Money Macro and Finance Research Group.
  4. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
  5. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Society for Computational Economics, vol. 28(1), pages 71-88, August.
  6. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
  7. Ryadh M. Alkhareif & William Barnett, 2012. "Divisia Monetary Aggregates for the GCC Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201209, University of Kansas, Department of Economics, revised Aug 2012.
  8. S. DeVicerte & P. Alvarez & J. Perez & C. Caso, 2008. "Does currency crisis identification matter?," Applied Financial Economics, Taylor & Francis Journals, vol. 18(5), pages 387-395.

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