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Detecting and forecasting business cycle turning points

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  • Harding, Don

Abstract

The R word has begun to appear in the media again bringing with it three technical questions viz, How will we know we are in recession? How will we know when it has ended? And How can we forecast its onset and ending? This paper does not provide answers to these questions rather it focuses on the technical issues that we need to resolve in order to provide good answers to these questions. The paper has three significant findings. First, the business cycle states obtained by the BBQ algorithm are complex statistical processes and it is not possible to write down an exact likelihood function for them. Second, for the classical and acceleration cycles it is possible to obtain a reasonably simple approximation to the BBQ algorithm that may permit one to write down a likelihood function. Third, when evaluating these algorithms there is a large di¤erence between the results using US GDP as compared to UK GDP or simulated data from models fit to US GDP. Specifically, turning points are much easier to detect in US GDP than in other series. One needs to take this into account when using US based research on detecting and forecasting business cycle turning points.

Suggested Citation

  • Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:33583
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    File URL: https://mpra.ub.uni-muenchen.de/33583/2/MPRA_paper_33583.pdf
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    References listed on IDEAS

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    1. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
    2. Burnside, Craig, 1998. "Detrending and business cycle facts: A comment," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 513-532, May.
    3. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    4. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    5. Don Harding & Adrian Pagan, 1999. "Knowing the Cycle," Melbourne Institute Working Paper Series wp1999n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. 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 249-279, April.
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    9. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
    10. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
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    Citations

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    Cited by:

    1. Tony Hall & Jan Jacobs & Adrian Pagan, "undated". "Macro-Econometric System Modelling @75," NCER Working Paper Series 95, National Centre for Econometric Research.
    2. Pagan, Adrian & Robinson, Tim, 2014. "Methods for assessing the impact of financial effects on business cycles in macroeconometric models," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 94-106.
    3. Fotis Papailias & Dimitrios D. Thomakos & Jiadong Liu, 2017. "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies," Empirical Economics, Springer, vol. 52(1), pages 255-282, February.
    4. Adrian Pagan, 2013. "Patterns and Their Uses," NCER Working Paper Series 96, National Centre for Econometric Research.
    5. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    6. Adrian Pagan & Tim Robinson, 2011. "Assessing Some Models of the Impact of Financial Stress upon Business Cycles," RBA Research Discussion Papers rdp2011-04, Reserve Bank of Australia.
    7. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 2(4), pages 1-20, October.
    8. Dimitrios D. Thomakos & Fotis Papailias, 2014. "“Out of Sync”: The Breakdown of Economic Sentiment Cycles in the EU," Review of International Economics, Wiley Blackwell, vol. 22(1), pages 131-150, February.

    More about this item

    Keywords

    Business cycle; turning points; forecasting; peak; trough;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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