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Predicting UK Business Cycle Regimes

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  • C R Birchenhall
  • D R Osborn
  • M Sensier

Abstract

This paper uses logistic regression to construct a one-quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one-quarter-ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short-term interest rates. The role of the latter emphasises the open nature of the UK economy.

Suggested Citation

  • C R Birchenhall & D R Osborn & M Sensier, 2000. "Predicting UK Business Cycle Regimes," Centre for Growth and Business Cycle Research Discussion Paper Series 02, Economics, The Univeristy of Manchester.
  • Handle: RePEc:man:cgbcrp:02
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    8. Karl Taylor & Robert McNabb, 2007. "Business Cycles and the Role of Confidence: Evidence for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 185-208, April.
    9. Wu-Jen Chuang & Liang-Yuh Ou-Yang & Wen-Chen Lo, 2009. "Nonlinear Market Dynamics Between Stock Returns And Trading Volume: Empirical Evidences From Asian Stock Markets," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice (1954-2015), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 56, pages 621-634, November.
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    11. Ana Beatriz C. Galvao, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487.
    12. E. Andersson & D. Bock & M. Frisen, 2006. "Some statistical aspects of methods for detection of turning points in business cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(3), pages 257-278.
    13. E. Andersson, 2002. "Monitoring cyclical processes. A non-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 973-990.
    14. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
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    16. Anthony Garratt & Donald Robertson & Stephen Wright, 2004. "Inside the black box: permanent vs transitory components and economic fundamentals," Money Macro and Finance (MMF) Research Group Conference 2003 35, Money Macro and Finance Research Group.
    17. Terence C. Mills & Ping Wang, 2003. "Multivariate Markov Switching Common Factor Models for the UK," Bulletin of Economic Research, Wiley Blackwell, vol. 55(2), pages 177-193, April.
    18. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.
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    20. Agne Reklaite, 2011. "Coincident, leading and recession indexes for the Lithuanian economy," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 91-108, July.
    21. Massimo Guidolin & Allan Timmermann, 2003. "Recursive Modeling of Nonlinear Dynamics in UK Stock Returns," Manchester School, University of Manchester, vol. 71(4), pages 381-395, July.

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