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An Artificial Neural Network System of Leading Indicators

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Abstract

We construct an artificial neural network to act as a system of leading indicators. We focus on radial basis functions as the architecture and forward selection as the method for determining the number of basis functions in the network. A brief review is given of the advantages of this as a strategy. Using common heuristics to determine scaling, radii and centre population, we find that the results for output growth prediction for six European countries are promising.

Suggested Citation

  • Andrew Blake, 1999. "An Artificial Neural Network System of Leading Indicators," National Institute of Economic and Social Research (NIESR) Discussion Papers 144, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:144
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    Cited by:

    1. Pelin Öge Güney, 2019. "Macroeconomic Uncertainty and Investment Relationship for Turkey," Working Papers 1332, Economic Research Forum, revised 21 Aug 2019.
    2. Joseph P. Byrne & E. Philip Davis, 2005. "The Impact of Short‐ and Long‐run Exchange Rate Uncertainty on Investment: A Panel Study of Industrial Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(3), pages 307-329, June.
    3. Amber Fatima & Abdul Waheed, 2014. "Economic uncertainty and growth performance: a macroeconomic modeling analysis for Pakistan," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1361-1387, May.
    4. Atreya Chakraborty & Christopher F. Baum & Boyan Liu, 2017. "Corporate financial policy and the value of cash under uncertainty," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 13(2), pages 149-164, April.
    5. International Monetary Fund, 2004. "United Kingdom: Selected Issues," IMF Staff Country Reports 2004/055, International Monetary Fund.
    6. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Documents de travail du Centre d'Economie de la Sorbonne 10065, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Christopher F. Baum & Atreya Chakraborty & Liyan Han & Boyan Liu, 2012. "The effects of uncertainty and corporate governance on firms’ demand for liquidity," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 515-525, February.
    8. Matthias Kredler, 2005. "Sector-Specific Volatility Patterns in Investment," Macroeconomics 0501016, University Library of Munich, Germany.
    9. Mourad Zmami & Ousama Ben-Salha, 2015. "The adjustment of plant-level investment to exchange rate fluctuations in Tunisia: do the size and the ownership structure matter?," Economics Bulletin, AccessEcon, vol. 35(4), pages 2487-2505.
    10. Eichler, Stefan & Littke, Helge C. N., 2017. "Central bank transparency and the volatility of exchange rates," IWH Discussion Papers 22/2017, Halle Institute for Economic Research (IWH).
    11. Chih-Chuan Yeh & Kuan-Min Wang & Yu-Bo Suen, 2011. "A quantile framework for analysing the links between inflation uncertainty and inflation dynamics across countries," Applied Economics, Taylor & Francis Journals, vol. 43(20), pages 2593-2602.
    12. Mohamed Arouri & Christophe Rault & Frédéric Teulon, 2014. "Economic policy uncertainty, oil price shocks and GCC stock markets," Economics Bulletin, AccessEcon, vol. 34(3), pages 1822-1834.

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