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Forecasting UK Industrial Production Over the Business Cycle

Author

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  • Denise R. Osborn

    (University of Manchester)

  • Paul W. Simpson

    (Department for Education and Employment)

Abstract

This paper examines the information available through leading indicators for modelling and forecasting the UK quarterly index of production (seasonally adjusted). The emphasis is on one-quarter ahead prediction, especially over the 1990s recession. Linear specifications considered are univariate autoregressive models together with dynamic single indicator and multiple indicator models. Both univariate and leading indicator versions of nonlinear Markov switching specifications are also examined. In the latter case, the transition probabilities are modelled as logistic functions of the leading indicators, allowing the lead times to differ for the expansion to expansion and recession to recession probabilities. Despite general evidence that the term structure of interest rates helps regime classification in the Markov switching models, these models perform relatively poorly in forecasting the 1990s production recession. It is suggested that this poor performance may be due to the nature of that recession, which differed from previous major UK postwar recessions in having no single quarter where industrial production declined substantially. However, a three indicator linear specification does well. The leading indicator variables in this latter model are a short-term interest rate, the stock market dividend yield and the optimism balance from the quarterly survey conducted by the Confederation of British Industry.

Suggested Citation

  • Denise R. Osborn & Paul W. Simpson, 2000. "Forecasting UK Industrial Production Over the Business Cycle," Econometric Society World Congress 2000 Contributed Papers 1059, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1059
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    References listed on IDEAS

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    12. Andreou, Elena & Osborn, Denise R & Sensier, Marianne, 2000. "A Comparison of the Statistical Properties of Financial Variables in the USA, UK and Germany over the Business Cycle," Manchester School, University of Manchester, vol. 68(4), pages 396-418, Special I.
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    Cited by:

    1. Bruno Giancarlo & Lupi Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," ISAE Working Papers 33, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. 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.
    3. Mirna Dumičić, 2014. "Financial Stress Indicators for Small, Open, Highly Euroised Countries – the Case of Croatia," Working Papers 41, The Croatian National Bank, Croatia.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    9. 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.
    10. Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.

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