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A Nonlinear Approach to Forecasting with Leading Economic Indicators

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  • Jagric Timotej

Abstract

The classical NBER leading indicators model was built solely within a linear framework. With recent developments in nonlinear time-series analysis, several authors have begun to examine the forecasting properties of nonlinear models in the field of forecasting business cycles. The research presented in this paper focuses on the development of a new approach to forecasting with leading indicators based on neural networks. Empirical results are presented for forecasting the Index of Industrial Production. The results demonstrate that a superior performance can be obtained relative to the classical model.

Suggested Citation

  • Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
    • Handle: RePEc:bpj:sndecm:v:7:y:2003:i:2:n:4
    DOI: 10.2202/1558-3708.1135
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    Cited by:

    1. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    2. Chian, Abraham C.-L. & Rempel, Erico L. & Rogers, Colin, 2006. "Complex economic dynamics: Chaotic saddle, crisis and intermittency," Chaos, Solitons & Fractals, Elsevier, vol. 29(5), pages 1194-1218.
    3. 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.

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