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Forecasting UK Industrial Production over the Business Cycle

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  • Simpson, Paul W
  • Osborn, Denise R
  • Sensier, Marianne

Abstract

This paper examines the information available through leading indicators for modelling and forecasting the UK quarterly index of production. Both linear and non-linear specifications are examined, with the latter being of the Markov-switching type as used in many recent business cycle applications. The Markov-switching models perform relatively poorly in forecasting the 1990s production recession, but a three-indicator linear specification does well. The leading indicator variables in this latter model include a short-term interest rate, the stock market dividend yield and the optimism balance from the quarterly CBI survey. Copyright © 2001 by John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:jof:jforec:v:20:y:2001:i:6:p:405-24
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    References listed on IDEAS

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    Cited by:

    1. Denise R. Osborn & Marianne Sensier, 2002. "The Prediction of Business Cycle Phases: Financial Variables and International Linkages," National Institute Economic Review, National Institute of Economic and Social Research, vol. 182(1), pages 96-105, October.
    2. Mirna Dumičić, 2014. "Financial Stress Indicators for Small, Open, Highly Euroised Countries – the Case of Croatia," Working Papers 41, The Croatian National Bank, Croatia.
    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. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    5. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
    6. 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.
    7. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," MPRA Paper 42332, University Library of Munich, Germany.
    8. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Yuan, Chunming, 2011. "Forecasting exchange rates: The multi-state Markov-switching model with smoothing," International Review of Economics & Finance, Elsevier, vol. 20(2), pages 342-362, April.
    10. Ioannis A. Venetis & David A. Peel & Ivan Paya, 2004. "Asymmetry in the link between the yield spread and industrial production: threshold effects and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 373-384.
    11. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    12. Miquel Clar & Juan-Carlos Duque & Rosina Moreno, 2007. "Forecasting business and consumer surveys indicators-a time-series models competition," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2565-2580.

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