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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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    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. 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.
    3. V. Colombo, 2020. "Opening the Red Budget Box: Nonlinear Effects of a Tax Shock in the UK," Working Papers wp1142, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Adrian Pagan, 2013. "Patterns and Their Uses," NCER Working Paper Series 96, National Centre for Econometric Research.
    5. 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.
    6. 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.
    7. 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.
    8. Baumann, Ursel & Gómez Salvador, Ramón & Seitz, Franz, 2018. "Global recessions and booms: What do probit models tell us?," Weidener Diskussionspapiere 61, University of Applied Sciences Amberg-Weiden (OTH).
    9. Baumann, Ursel & Gomez-Salvador, Ramon & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
    10. 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.
    11. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," IJFS, MDPI, vol. 2(4), pages 1-20, October.

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    More about this item

    Keywords

    Business cycle; turning points; forecasting; peak; trough;
    All these keywords.

    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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