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.
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Volume (Year): 37 (2005)
Issue (Month): 6 ()
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- 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 383-97, August.
- Johansen, S., 1991. "Determination of Cointegration Rank in the Presence of a Linear Trend," Papers 76a, Helsinki - Department of Economics.
- Gordon De Brouwer & Neil R. Ericsson, 1995.
"Modelling inflation in Australia,"
International Finance Discussion Papers
530, Board of Governors of the Federal Reserve System (U.S.).
- Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
- 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.
- Jurgen A. Doornik & David F. Hendry & Bent Nielsen, 1998.
"Inference in Cointegrating Models: UK M1 Revisited,"
Journal of Economic Surveys,
Wiley Blackwell, vol. 12(5), pages 533-572, December.
- 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 533-72, December.
- Drake, L. & Mullineux, A., 1995. "One Divisa Money for Europe?," Discussion Papers 95-04, Department of Economics, University of Birmingham.
- JØrgen Wolters & Helmut LØtkepohl, 1998. "A money demand system for German M3," Empirical Economics, Springer, vol. 23(3), pages 371-386.
- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, December.
- 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.
- 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.
- Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
- Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
- 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.
- Luca Stanca, 1999. "Asymmetries and nonlinearities in Italian macroeconomic fluctuations," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 483-491.
- 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.
- 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.
- James H. Stock & Mark W. Watson, 1999.
NBER Working Papers
7023, National Bureau of Economic Research, Inc.
- 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.
- Perman, Roger & Scouller, John, 1999. "Business Economics," OUP Catalogue, Oxford University Press, number 9780198775249, December.
- Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(02), pages 256-271, August.
- 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.
- 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 501-11, November.
- 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 664-71, November.
- William Barnett, 2005.
- William Barnett, 2005. "Monetary Aggregation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200510, University of Kansas, Department of Economics, revised Mar 2005.
- 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.
- 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.
- 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.
- Stracca, Livio, 2001. "Does liquidity matter? Properties of a synthetic divisia monetary aggregate in the euro area," Working Paper Series 0079, European Central Bank.
- Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
- 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, 04.
- 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 435-49, September.
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