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A Nonlinear Extension Of The Nber Model For Short‐Run Forecasting Of Business Cycles

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  • TIMOTEJ JAGRIC
  • SEBASTJAN STRASEK

Abstract

To avoid the pitfalls of the widely used NBER model, in this paper we have adopted neural networks to forecast business cycles. We find that our model has overcome some of the main deficiencies of the classical leading indicators model: first, the model was able to correctly forecast all reference points in in‐sample and out‐of‐sample data; second, the model can forecast the future value of reference series; and third, the model has a constant forecast horizon. Sensitivity analysis suggests there are some nonlinear relationships between the reference variable and selected leading indicators. This explains why we were able to improve the forecasting performance of the original model.

Suggested Citation

  • Timotej Jagric & Sebastjan Strasek, 2005. "A Nonlinear Extension Of The Nber Model For Short‐Run Forecasting Of Business Cycles," South African Journal of Economics, Economic Society of South Africa, vol. 73(3), pages 435-448, September.
    • Handle: RePEc:bla:sajeco:v:73:y:2005:i:3:p:435-448
    DOI: 10.1111/j.1813-6982.2005.00029.x
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    References listed on IDEAS

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    1. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Discussion Papers of DIW Berlin 207, DIW Berlin, German Institute for Economic Research.
    2. Wojciech W. Charemza & Derek F. Deadman, 1992. "New Directions In Econometric Practice," Books, Edward Elgar Publishing, number 84.
    3. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
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    2. Fullerton, Thomas M., Jr. & Mukhopadhyay, Somnath, 2013. "Border Region Bridge and Air Transport Predictability," MPRA Paper 59583, University Library of Munich, Germany, revised 11 Jul 2013.

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