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Forecasting The Emu Inflation Rate: Linear Econometric Vs. Non-Linear Computational Models Using Genetic Neural Fuzzy Systems

In: Applications of Artificial Intelligence in Finance and Economics

Author

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  • Stefan Kooths
  • Timo Mitze
  • Eric Ringhut

Abstract

This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according to a battery of parametric and non-parametric test statistics to measure their performance in one- and four-step ahead forecasts of quarterly data. Using genetic-neural fuzzy systems we find the computational approach superior to some degree and show how to combine both techniques successfully.

Suggested Citation

  • Stefan Kooths & Timo Mitze & Eric Ringhut, 2004. "Forecasting The Emu Inflation Rate: Linear Econometric Vs. Non-Linear Computational Models Using Genetic Neural Fuzzy Systems," Advances in Econometrics, in: Applications of Artificial Intelligence in Finance and Economics, pages 145-173, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(04)19006-3
    DOI: 10.1016/S0731-9053(04)19006-3
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