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Forecasting UK Industrial Production Over the Business Cycle

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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.

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1059.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:1059

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  1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  2. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-44, October.
  3. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
  4. Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-55, December.
  5. Driffill, John & Sola, Martin, 1998. "Intrinsic bubbles and regime-switching," Journal of Monetary Economics, Elsevier, vol. 42(2), pages 357-373, July.
  6. Artis, Michael J, et al, 1995. "Turning Point Prediction for the UK Using CSO Leading Indicators," Oxford Economic Papers, Oxford University Press, vol. 47(3), pages 397-417, July.
  7. Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.
  8. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
  9. Engle, Robert F., 1982. "A general approach to lagrange multiplier model diagnostics," Journal of Econometrics, Elsevier, vol. 20(1), pages 83-104, October.
  10. 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.
  11. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  12. Artis, Michael J & Kontolemis, Zenon G & Osborn, Denise R, 1997. "Business Cycles for G7 and European Countries," The Journal of Business, University of Chicago Press, vol. 70(2), pages 249-79, April.
  13. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Modelling Business Cycle Movements in the UK Economy," Economica, London School of Economics and Political Science, vol. 68(270), pages 243-67, May.
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Cited by:
  1. 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.
  2. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," MPRA Paper 42332, University Library of Munich, Germany.
  3. Giancarlo Bruno, 2009. "Non-linear relation between industrial production and business surveys data," ISAE Working Papers 119, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  4. 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.
  5. 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.
  6. 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.

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