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

Applied Economics

Volumn 37, Issue 6, 2005, Pages 665-680

  Binner, Jane M ^a   Bissoondeeal, Rakesh K ^b   Elger, Thomas ^c   Gazely, Alicia M ^b   Mullineux, Andrew W ^d  

^a ASTON UNIVERSITY   (United Kingdom)

^b NOTTINGHAM TRENT UNIVERSITY   (United Kingdom)

^c LUND UNIVERSITY   (Sweden)

^d UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; EUROPEAN MONETARY UNION; FORECASTING METHOD; INFLATION; METHODOLOGY;

EASTERN HEMISPHERE; EURASIA; EUROPE; WORLD;

EID: 17744399299     PISSN: 00036846     EISSN: None     Source Type: Journal    
DOI: 10.1080/0003684052000343679     Document Type: Article

References (74)

1. Adya, M. and Calopy, F. (1998) How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of Forecasting, 17, 481-95.
2. Balkin, S. D. and Ord, J. K. (2000) Automatic neural network modeling for univariate time series, International Journal of Forecasting, 16, 509-15.
3. Barnett, W. A. (1980) Economic monetary aggregates: an application of the index numbers and aggregation theory, Journal of Econometrics, 14, 11-48. Reprinted in The Theory of Monetary Aggregation (Eds) W. A. Barnett and A. Serletis, North Holland, Amsterdam, pp. 11-48.
4. Barnett, W. A. (1982) The optimal level of monetary aggregation, Journal of Money, Credit and Banking, 14, 687-710.
5. Barnett, W. A and Chen, P. (1986) Economic theory as a generator of measurable attractors, Mondes en Develppement 14. Reprinted in Laws of Nature and Human Conduct: Specificities and Unifying Themes (Eds) I. Prigogine and M. Sanglier, G.O.R.D.E.S., Brussels, pp. 209-24.
6. Barnett, W. A. and Chen, P. (1988a) The aggregation-theoretic monetary aggregates are chaotic and have strange attractors: an econometric application of mathematical chaos, in Dynamic Econometric Modeling, Proceedings of the Third International Symposium on Economic and Econometrics (Eds) W. A. Barnett, E. R. Berndt and H. White, Cambridge University Press, UK, pp. 199-245.
7. Barnett, W. A. and Chen, P. (1988b) Deterministic chaos and fractal attractors as tools for nonparametric econometric cinference, Dynamical Computer Modeling, 10, 275-96.
8. Barnett, W. A. and Hinich, M. J. (1992) Empirical chaotic dynamics in economics, Annals of Operation Research, 37, 1-15.
9. Barnett, W. A. and Hinich, M. J. (1993) Has chaos been discovered with economic data?, in Evolutionary Dynamics and Non-linear Economics (Eds) P. Chen and R. Day, Oxford University Press, New York, pp. 254-63.
10. Barnett, W. A., Fisher, D. and Serletis, A. (1992) Consumer theory and the demand for money, Journal of Economic Literature, 30, 2086-119. Reprinted in The Theory of Monetary Aggregation (Eds) W. A. Barnett and A. Serletis, North Holland, Amsterdam, pp. 389-427.
11. Barron, A. R. (1994) A comment on 'Neural networks: a review from a statistical perspective', Statistical Science, 9, 33-5.
12. Binner, J. M. and Gazely, A. M. (1999) A neural network approach to inflation forecasting: the case of Italy, Global Business and Economics Review, 1, 76-92.
13. Binner, J. M., Gazely, A. M. and Chen, S. (2002) Financial innovation and Divisia indices in Taiwan: a neural network approach, European Journal of Finance, 8, 238-47.
14. Binner, J. M., Gazely, A. M., Chen S. H. and Chie, B. T. (2004) Financial innovation and Divisia money in Taiwan: comparative evidence from neural networks and vector error correction forecasting models, Contemporary Economic Policy, 22(2), 213-24.
15. Bishop, C. M. (1995) Neural Networks for Pattern Recognition, Oxford University Press, New York.
16. Box, G. P. and Jenkins, G. M. (1970) Time Series Analysis and Forecasting: the Box-Jenkins Approach, Holden-Day, San Francisco.
17. Chen, P. (1988) Empirical and theoretical evidence of economic chaos, Systems Dynamic Review, 4, 81-108.
18. Chrystal, K. A. and MacDonald, R. (1994) Empirical evidence on the recent behavior and usefulness of Simple Sum and weighted measures of money stock, Federal Reserve Bank of St. Louis Review, 76 2), 73-109.
19. Church, K. B. and Curram, S. P. (1996) Forecasting consumers' expenditure: a comparison between econometric and neural network models, International Journal of Forecasting, 12, 255-67.
20. Cubiles-de-la-Vega, M., Pino-Mejías, R., Pascual-Acosta, A. and Munoz-García, J. (2002) Building neural network forecasting models from time series ARIMA models: a procedure and a comparative analysis, Intelligent Data Analysis, 6, 53-95.
21. De Brouwer, G. and Ericsson, N. R. (1998) Modeling inflation in Australia, Journal of Business and Economics, 16, 433-49.
22. DeCoster, G. P. and Mitchell, D. W. (1991) Non-linear monetary dynamics, Journal of Business and Economics Statistics, 9, 455-61.
23. Dickey, D. A. and Fuller, W. A. (1979) Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427-31.
24. Doornik, J. A., Nielsen, B. and Hendry, D. F. (1998) Inference in cointegrating models: UK M1 revisited, Journal of Economic Surveys 12, 533-72.
25. Dorsey, R. E. (2000) Neural networks with Divisia money: better forecasts of future inflation?, in Divisia Monetary Aggregates: Theory and Practice (Eds) M. T. Belongia and J. M. Binner, Palgrave, New York, pp. 28-43.
26. Drake, L. and Mills, T. C. (2002) Is M3 an appropriate aggregate for guiding ECB monetary policy, Loughborough University Working Paper.
27. Drake, L., Chrystal, K. A. and Binner, J. M. (2000) Weighted monetary aggregates for the UK, in Divisia Monetary Aggregates: Theory and Practice (Eds) M. T. Belongia and J. M. Binner, Palgrave, New York, pp. 47-78.
28. Drake, L., Mullineux, A. W. and Agung, J. (1997) One Divisia money for Europe, Applied Economics, 29, 775-86.
29. ECB (1999a) The stability-oriented monetary policy strategy of the Eurosytem, ECB Monthly Bulletin. January, 39-50.
30. ECB (1999b) Euro area monetary aggregates and their role in the Eurosystem's monetary policy strategy, ECB Monthly Bulletin, February, 29-46.
31. ECB (2000) The two pillars of the ECBs monetary policy strategy, ECB Monthly Bulletin, November, 37-48.
32. Fair, C. R. and Shiller, R. (1990) Comparing information in forecasts from econometric models, The American Economic Review, 80, 375-89.
33. Faraway, J. and Chatfield, C. (1998) Time series forecasting with neural networks: a comparative study using airline data, Applied Statistics 47, 231-50.
34. Gately, E. (1996) Neural Networks for Financial Forecasting, John Wiley, New York.
35. Gazely, A. M. and Binner J. M. (1998) Better forecasts of future inflation? The neural network approach, Internet Journal of Financial Information Systems, February.
36. Gazely, A. M. and Binner, J. M. (2000) The application of neural networks to the Divisia index debate: evidence from three countries, Applied Economics, 32, 1607-15.
37. Gorr, W. L. and Nagin, D. and Szcypula, J. (1994) Comparative study of artificial neural network and statistical models for predicting student grade point averages, International Journal of Forecasting, 10 17-34.
38. Granger, C. W. J. (1981) Some properties of time series data and their use in econometric model specification, Journal of Econometrics, 16, 121-30.
39. Haggan, V. and Ozaki, T. (1981) Modeling non-linear vibrations using an amplitude-dependent autoregressive time series model, Biometrika, 68, 189-96.
40. Hansen, H. and Juselius, K. (1995) Cats in Rats. Cointegration Analysis of Time Series, Estima, Evaston, IL, USA.
41. Hendry, D. F. (1993) Econometrics: Alchemy or Science?, Blackwell Publishers, Oxford.
42. Hendry, D. F. (1995) Dynamic Econometrics, Oxford University Press, Oxford.
43. Hendry, D. F. and Doornik, J. A. (1994) Modelling linear dynamic econometric systems, Scottish Journal of Political Economy, 41 1-33.
44. Herrmann, H., Reimers, H. and Toedter, K. (2000) Weighted monetary aggregates for Germany, in Divisia Monetary Aggregates (Eds) M. T. Belongia and J. M. Binner, Palgrave, New York, pp. 79-101.
45. Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-66.
46. Johansen, S. (1988) Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12, 231-54.
47. Johansen, S. (1992) Determination of cointegration rank in the presence of a linear trend, Oxford Bulletin of Economics and Statistics, 54, 383-97.
48. Johansen, S. (1995) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press, Oxford.
49. Johansen, S. and Juselius, K. (1990) Maximum likelihood estimation and inference on cointegration with application to the demand for money, Oxford Bulletin of Economics and Statistics, 52, 169-209.
50. Johnes, G. (2000) Up around the bend: linear and non-linear models of the UK economy compared, International Review of Applied Economics 14, 485-93.
51. Krunic, S. Krcmar, I. and Rajakovic, N. (2000) An improved neural network application for short-term load forecasting in power systems, Electric Machines and Power Systems, 28, 703-21.
52. Lesage, J. P. (1990) A comparison of the forecasting ability of the ECM and VAR models, Review of Economics and Statistics, 72, 664-71.
53. Lutkepohl, H. and Wolters, J. (1998) A money demand system for German M3, Empirical Economics, 23, 371-86.
54. Mackinnon, J. G. (1991) Critical values for cointegration tests, in Long Run Equilibrium Relationships (Eds) R.F. Engle and C. W. J. Granger, Oxford University Press, Oxford, pp. 267-76.
55. Makridakis, S., Wheelright, S. C. and McGee, V. E. (1983) Forecasting Methods and Applications, 2nd edn, John Wiley, New York.
56. Masters, T. (1993) Practical Neural Network Recipes in C++, Academic Press, London.
57. Monterola, C., Lim, M., Garcia, J. and Saloma, C. (2002) Accurate forecasting of the undecided population in a public opinion poll, Journal of Forecasting, 21, 435-49.
58. Moshiri, S. and Cameron, N. (2000) Neural networks versus econometric models in inflation forecasting, Journal of Forecasting, 19, 201-17.
59. Nag, A. K. and Mitra, A. (2002) Forecasting daily foreign exchange rates using genetically optimized neural networks, Journal of Forecasting 21, 501-11.
60. Pantula, G. (1989) Testing for unit roots in the time series data, Econometric Theory, 5, 256-71.
61. Plasmans, J., Verkooijen, W. and Daniels, H. (1998) Estimating structural exchange rate models by artificial neural networks, Applied Financial Economics, 8, 541-51.
62. Rumelhart, D. E., Durbin, R., Golden, R. and Chauvin, Y. (1995) Backpropagation: the basic theory, in Backpropagation: Theory, Architectures, and Applications (Eds) Y. Chauvin and D. E. Rumelhart, Lawrence Erlbaum Associates, NJ.
63. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning internal representations by error propagation, in Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vol 1 (Eds) D. E. Rumelhart and J. L. McClelland, MIT Press, Cambridge, MA.
64. Shazly, M. R. E. and Shazly, H. E. E. (1999) Forecasting currency prices cusing genetically evolved neural network architecture, International Review of Financial Analysis, 8, 67-82.
65. Shoesmith, G. L. (1992) Cointegration, error correction and improved regional VAR forecasting, Journal of Forecasting, 11, 91-109.
66. Shoesmith, G. L. (1995) Long term forecasting of non-cointegrated and cointegrated regional and national models, Journal of Regional Science, 35, 43-64.
67. Stanca, L. (1999) Asymmetries and non-linearities in Italian macroeconomic fluctuations, Applied Economics, 31, 483-99.
68. Stock, J. H. and Watson, M. W. (1999) Forecasting inflation, Journal of Monetary Economics, 44, 2 93-335.
69. Stracca, L. (2004) Does liquidity matter?: Properties of a synthetic Divisia monetary aggregate in the Euro area, Oxford Bulletin of Economics and Statistics, 66, 309-31.
70. Tiao, G. C. and Tsay, R. S. (1994) Some advances in non-linear and adaptive modelling in time-series, Journal of Forecasting, 13 109-31.
71. Tkacz, G. (2001) Neural network forecasting of Canadian GDP growth, International Journal of Forecasting, 17, 57-69.
72. Tong, H. (1990) Non-linear Time Series. A Dynamical System Approach Oxford University Press, Oxford, UK.
73. Warner, B. and Mishra, M. (1996). Understanding neural networks as statistical tools, The American Statistician, 50, 284-93.
74. Zhang, G., Patuwo, B. E. and Hu, Y. H. (1998) Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14, 17-34.