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Turning Point Prediction for the UK using CSO Leading Indicators

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  • Artis, Michael J

Abstract

This paper examines the performance of alternative models for predicting turning points in the UK growth cycle. The models are based upon an interpretation of movements in the CSO's composite longer and shorter leading indicators. The difference between the models lies in the choice of method adopted for separating and classifying observations into a pattern corresponding to an upturn and downturn regime, together with the decision rule applied in recognizing when a regime shift has occurred. The models involved include a simple mechanical rule based upon an interpretation of consecutive movements in the leading indicator and two probabilistic methods, namely a standard Bayesian model and the sequential probability model developed by Neftci (1982). The results of the exercise suggest that usefulness of the shorter leading index is extremely limited; prediction based upon this series is typically outperformed by naive, non-indicator methods. The information content of the longer leading index appears somewhat greater. The signal extracted by the sequential probability model is particularly well-defined in this respect giving rise to a lead time of between four and six months at peaks and six months for troughs. At horizons beyond six months, however, the sequential probability model is outperformed by a more conventional Bayesian method.

Suggested Citation

  • Artis, Michael J, 1993. "Turning Point Prediction for the UK using CSO Leading Indicators," CEPR Discussion Papers 833, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:833
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    Cited by:

    1. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Forecasting UK Industrial Production over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 405-424, September.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    4. Croce, Roberto M. & Haurin, Donald R., 2009. "Predicting turning points in the housing market," Journal of Housing Economics, Elsevier, vol. 18(4), pages 281-293, December.
    5. Simon Hayes, 2001. "Leading indicator information in UK equity prices: an assessment of economic tracking portfolios," Bank of England working papers 137, Bank of England.
    6. Jacques Anas & Muriel Nguiffo-Boyom, 2001. "A New Indicator Based on Neftçi's Approach for Predicting Turning Points of the Euro-Zone Growth Cycle," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 70(3), pages 364-376.
    7. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    8. Michele Fratianni & Michael Artis, 1996. "The lira and the pound in the 1992 currency crisis: Fundamentals or speculation?," Open Economies Review, Springer, vol. 7(1), pages 573-589, March.

    More about this item

    Keywords

    Prediction; Sequential Probability; Turning Points;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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