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


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


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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  • Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
  • Handle: RePEc:taf:applec:v:37:y:2005:i:6:p:665-680
    DOI: 10.1080/0003684052000343679

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    References listed on IDEAS

    1. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    2. Balkin, Sandy D. & Ord, J. Keith, 2000. "Automatic neural network modeling for univariate time series," International Journal of Forecasting, Elsevier, vol. 16(4), pages 509-515.
    3. Church, Keith B. & Curram, Stephen P., 1996. "Forecasting consumers' expenditure: A comparison between econometric and neural network models," International Journal of Forecasting, Elsevier, vol. 12(2), pages 255-267, June.
    4. Johansen, Soren, 1992. "Determination of Cointegration Rank in the Presence of a Linear Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 383-397, August.
    5. JØrgen Wolters & Helmut LØtkepohl, 1998. "A money demand system for German M3," Empirical Economics, Springer, vol. 23(3), pages 371-386.
    6. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    7. Doornik, Jurgen A & Hendry, David F & Nielsen, Bent, 1998. " Inference in Cointegrating Models: UK M1 Revisited," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 533-572, December.
    8. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    9. de Brouwer, Gordon & Ericsson, Neil R, 1998. "Modeling Inflation in Australia," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 433-449, October.
    10. Perman, Roger & Scouller, John, 1999. "Business Economics," OUP Catalogue, Oxford University Press, number 9780198775249.
    11. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    12. William Barnett, 2005. "Monetary Aggregation," Macroeconomics 0503017, EconWPA.
    13. Hendry, David F & Doornik, Jurgen A, 1994. "Modelling Linear Dynamic Econometric Systems," Scottish Journal of Political Economy, Scottish Economic Society, vol. 41(1), pages 1-33, February.
    14. Barnett, William A., 1980. "Economic monetary aggregates an application of index number and aggregation theory," Journal of Econometrics, Elsevier, vol. 14(1), pages 11-48, September.
    15. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    16. Drake, L. & Mullineux, A., 1995. "One Divisa Money for Europe?," Discussion Papers 95-04, Department of Economics, University of Birmingham.
    17. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    18. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    19. Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(02), pages 256-271, August.
    20. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    21. Stracca, Livio, 2001. "Does liquidity matter? Properties of a synthetic divisia monetary aggregate in the euro area," Working Paper Series 0079, European Central Bank.
    22. Jane M. Binner & Alicia M. Gazely & Shu-Heng Chen & Bin-Tzong Chie, 2004. "Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error-Correction Forecasting Models," Contemporary Economic Policy, Western Economic Association International, vol. 22(2), pages 213-224, April.
    23. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    24. Luca Stanca, 1999. "Asymmetries and nonlinearities in Italian macroeconomic fluctuations," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 483-491.
    25. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
    26. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
    27. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    28. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    29. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-671, November.
    30. Monterola, Christopher, et al, 2002. "Accurate Forecasting of the Undecided Population in a Public Opinion Poll," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(6), pages 435-449, September.
    31. A. M. Gazely & J. M. Binner, 2000. "The application of neural networks to the Divisia index debate: evidence from three countries," Applied Economics, Taylor & Francis Journals, vol. 32(12), pages 1607-1615.
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    Cited by:

    1. 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.
    2. 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 & Francis Journals, vol. 37(11), pages 1257-1266.
    3. 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.
    4. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    5. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    7. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    8. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    9. repec:lrk:eeaart:35_2_5 is not listed on IDEAS
    10. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk-Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642.
    11. 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.
    12. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    13. 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.
    14. Rakesh Bissoondeeal & Michail Karoglou & Andy Mullineux, 2014. "Breaks in the UK Household Sector Money Demand Function," Manchester School, University of Manchester, vol. 82, pages 47-68, December.
    15. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.

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