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A new approach to dating and predicting Australian business cycle phase changes

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  • Allan Layton

Abstract

Due to well-known lags, counter-cyclical macroeconomic policies often exacerbate, rather than ameliorate, business cycles. Early recognition of upcoming phase shifts, particularly contractions, may assist in fine-tuning such policies. This objective is pursued in the paper by applying Hamilton's (1989, 1990, 1991) quasi-Bayesian, Markovian, regime-switching model to monthly growth rates of leading, long-leading and coincident indexes of Australian economic activity. A simple rule applied to regime probabilities for each data point of the coincident index produces a phase chronology that is very similar to that produced by the Bry and Boschan (1971) turning point algorithm. The regime switching model is also applied to the leading and long-leading indexes. The application of a simple rule to the resultant regime probabilities is found to result in a potentially very reliable advance signalling system for Australian business cycle phase changes.

Suggested Citation

  • Allan Layton, 1997. "A new approach to dating and predicting Australian business cycle phase changes," Applied Economics, Taylor & Francis Journals, vol. 29(7), pages 861-868.
    • Handle: RePEc:taf:applec:v:29:y:1997:i:7:p:861-868
    DOI: 10.1080/000368497326516
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    Cited by:

    1. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    2. Pontines, Victor, 2017. "The financial cycles in four East Asian economies," Economic Modelling, Elsevier, vol. 65(C), pages 51-66.
    3. Paul Cashin & Sam Ouliaris, 2004. "Key Features of Australian Business Cycles," Australian Economic Papers, Wiley Blackwell, vol. 43(1), pages 39-58, March.
    4. Allan P. Layton, 1997. "Do Leading Indicators Really Predict Australian Business Cycle Turning Points?," The Economic Record, The Economic Society of Australia, vol. 73(222), pages 258-269, September.
    5. Philip M. Bodman, 1998. "Asymmetry and Duration Dependence in Australian GDP and Unemployment," The Economic Record, The Economic Society of Australia, vol. 74(227), pages 399-411, December.
    6. Narayan, Paresh Kumar, 2008. "An investigation of the behaviour of Australia's business cycle," Economic Modelling, Elsevier, vol. 25(4), pages 676-683, July.
    7. Taylor, Andrew & Shepherd, David & Duncan, Stephen, 2005. "The structure of the Australian growth process: A Bayesian model selection view of Markov switching," Economic Modelling, Elsevier, vol. 22(4), pages 628-645, July.
    8. Milan Christian de Wet, 2021. "Modelling the Australasian Financial Cycle: A Markov-Regime Switching Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 14(1), pages 69-79, June.
    9. Sanidas, Elias, 2005. "The Australian Dollar's Long-Term Fluctuations and Trend: The Commodity Prices-cum-Economic Cycles Hypothesis," Economics Working Papers wp05-29, School of Economics, University of Wollongong, NSW, Australia.
    10. Ming-Yuan Leon Li & Hsiou-Wei William Lin & Rau Hsiu-hua, 2005. "The performance of the Markov-switching model on business cycle identification revisited," Applied Economics Letters, Taylor & Francis Journals, vol. 12(8), pages 513-520.
    11. Tan, Hao & Mathews, John A., 2010. "Identification and analysis of industry cycles," Journal of Business Research, Elsevier, vol. 63(5), pages 454-462, May.
    12. Hao Tan & John A. Mathews, 2007. "Cyclical Dynamics in Three Industries," DRUID Working Papers 07-07, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    13. Sanidas, Elias, 2014. "Four harmonic cycles explain and predict commodity currencies' wide long term fluctuations," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 135-151.

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