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Forecasting with leading economic indicators - a non-linear approach

Author

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  • Timotej Jagric

Abstract

Leading economic indicators have a long tradition in forecasting future economic activity. Recent developments, however, suggest that there is scope for adding extensions to the methodology of forecasting major economic fluctuations. In this paper, the author tries to develop a new model, which would outperform the forecast accuracy of classical leading indicators model. The use of artificial neural networks is proposed here. For demonstration a case study for Slovene economy is included. The main finding is that, at the twelve months forecasting horizon, a stable and improved forecast accuracy could be achieved for in- and out-of-sample data.

Suggested Citation

  • Timotej Jagric, 2003. "Forecasting with leading economic indicators - a non-linear approach," Prague Economic Papers, University of Economics, Prague, vol. 2003(1).
  • Handle: RePEc:prg:jnlpep:v:2003:y:2003:i:1:id:207
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    More about this item

    Keywords

    leading economic indicators; neural network; forecasting; aggregate economic activity;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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