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Assessing the Synchronicity and Nature of Australian State Business Cycles

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  • Aubrey Poon

Abstract

This paper assesses the synchronicity and nature of Australian state business cycles. To this end, I develop a time‐varying parameter panel Bayesian vector autoregression (BVAR) with a novel common stochastic volatility factor in the error structure, which is estimated in an efficient Markov chain Monte Carlo algorithm. The common stochastic volatility factor reveals that macroeconomic volatility was more pronounced during the Asian financial crisis than during the more recent global financial crisis. Next, the panel BVAR's common, state‐ and variable‐specific indicators capture several interesting economic facts. In particular, the fluctuations of the common indicator closely follow the trend line of the Organisation for Economic Co‐operation and Development composite leading indicators for Australia, making it a good proxy for nationwide business cycle fluctuations. Furthermore, despite significant co‐movements of Australian state and territory business cycles during times of economic contractions, the state indicators suggest that the average degree of synchronisation across the Australian state and territory cycles in the 2000s is only half of that in the 1990s. Given that aggregate macroeconomic activity is determined by cumulative activity of each of the states, the results suggests that the federal government should consider granting state governments greater autonomy in handling state‐specific cyclical fluctuations.

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  • Aubrey Poon, 2018. "Assessing the Synchronicity and Nature of Australian State Business Cycles," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 372-390, December.
  • Handle: RePEc:bla:ecorec:v:94:y:2018:i:307:p:372-390
    DOI: 10.1111/1475-4932.12441
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    Cited by:

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    2. Desiree M. Kunene & Renee van Eyden & Petre Caraiani & Rangan Gupta, 2023. "The Predictive Impact of Climate Risk on Total Factor Productivity Growth: 1880-2020," Working Papers 202321, University of Pretoria, Department of Economics.
    3. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    5. Jamie L. Cross & Aubrey Poon, 2020. "On the contribution of international shocks in Australian business cycle fluctuations," Empirical Economics, Springer, vol. 59(6), pages 2613-2637, December.
    6. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    7. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    8. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    9. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    10. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).

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