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On the China factor in international oil markets: A regime switching approach

Author

Listed:
  • Jamie L. Cross
  • Chenghan Hou
  • Bao H. Nguyen
Abstract
We investigate the relationship between world oil markets and China's macroeconomic performance over the past two decades. Our analysis starts by proposing a simple method for disentangling real economic activity stemming from China and the rest of the world. We then consider a sufficiently large set of dynamic VAR models to distinguish between abrupt and gradual changes in the macroeconomic relationships and volatility clustering in the shocks. A model exercise shows that a Markov-switching model is preferred to previously used models in the literature. When investigating the role of oil market shocks on China's output, we find that oil supply shocks tend to elicit a positive response, while the response of oil demand shocks is negative. Next, when analyzing world oil price dynamics, we find that demand shocks have had significant positive impacts over the past two decades. The average proportion of oil price variation explained by demand from China and rest of world demand are around 30 percent over the sample period. Importantly, while China specific effects are relatively constant, rest of world aggregate demand shocks are found to have larger impact during times of global macroeconomic downturn. This highlights the importance of our model comparison exercise. Finally, we find that the recent 2014/15 oil price drop was due to a combination of increased oil supply and decreased demand from China.

Suggested Citation

  • Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0069
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    References listed on IDEAS

    as
    1. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    2. Christiane Baumeister & Gert Peersman, 2013. "Time-Varying Effects of Oil Supply Shocks on the US Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 1-28, October.
    3. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    4. Christiane Baumeister & Gert Peersman & Ine Van Robays, 2010. "The Economic Consequences of Oil Shocks: Differences across Countries and Time," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    5. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "What Drives Oil Prices? Emerging Versus Developed Economies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1013-1028, November.
    6. Tang, Weiqi & Wu, Libo & Zhang, ZhongXiang, 2010. "Oil price shocks and their short- and long-term effects on the Chinese economy," Energy Economics, Elsevier, vol. 32(Supplemen), pages 3-14, September.
    7. Boqiang Lin & Jianglong Li, 2015. "The Determinants of Endogenous Oil Price: Considering the Influence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(5), pages 1034-1050, September.
    8. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.
    9. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    10. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    11. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    12. Gert Peersman, 2005. "What caused the early millennium slowdown? Evidence based on vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 185-207.
    13. Raymond, Jennie E & Rich, Robert W, 1997. "Oil and the Macroeconomy: A Markov State-Switching Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(2), pages 193-213, May.
    14. Zhao, Lin & Zhang, Xun & Wang, Shouyang & Xu, Shanying, 2016. "The effects of oil price shocks on output and inflation in China," Energy Economics, Elsevier, vol. 53(C), pages 101-110.
    15. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    16. Nigar Hashimzade & Michael A. Thornton (ed.), 2013. "Handbook of Research Methods and Applications in Empirical Macroeconomics," Books, Edward Elgar Publishing, number 14327.
    17. Helmut Lütkepohl & Aleksei NetŠunajev, 2014. "Disentangling Demand And Supply Shocks In The Crude Oil Market: How To Check Sign Restrictions In Structural Vars," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 479-496, April.
    18. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    19. Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.
    20. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    21. Raymond, Jennie E & Rich, Robert W, 1997. "Erratum [Oil and the Macroeconomy: A Markov State-Switching Approach]," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(4), pages 555-555, November.
    22. Hans-Martin Krolzig & Michael P. Clements, 2002. "Can oil shocks explain asymmetries in the US Business Cycle?," Empirical Economics, Springer, vol. 27(2), pages 185-204.
    23. Holm-Hadulla, Fédéric & Hubrich, Kirstin, 2017. "Macroeconomic implications of oil price fluctuations: a regime-switching framework for the euro area," Working Paper Series 2119, European Central Bank.
    24. Du, Limin & Yanan, He & Wei, Chu, 2010. "The relationship between oil price shocks and China's macro-economy: An empirical analysis," Energy Policy, Elsevier, vol. 38(8), pages 4142-4151, August.
    25. Chan, Joshua C.C. & Grant, Angelia L., 2015. "Pitfalls of estimating the marginal likelihood using the modified harmonic mean," Economics Letters, Elsevier, vol. 131(C), pages 29-33.
    26. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    27. Hou, Chenghan & Nguyen, Bao H., 2018. "Understanding the US natural gas market: A Markov switching VAR approach," Energy Economics, Elsevier, vol. 75(C), pages 42-53.
    28. Francesco Lippi & Andrea Nobili, 2012. "Oil And The Macroeconomy: A Quantitative Structural Analysis," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1059-1083, October.
    29. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    30. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    31. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    32. Kalouptsidi, Myrto, 2017. "Detection and Impact of Industrial Subsidies: The Case of Chinese Shipbuilding," CEPR Discussion Papers 12080, C.E.P.R. Discussion Papers.
    33. Wu, Gang & Zhang, Yue-Jun, 2014. "Does China factor matter? An econometric analysis of international crude oil prices," Energy Policy, Elsevier, vol. 72(C), pages 78-86.
    34. Xiaoyi Mu & Haichun Ye, 2011. "Understanding the Crude Oil Price: How Important Is the China Factor?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 69-92.
    35. Liu, Li & Wang, Yudong & Wu, Chongfeng & Wu, Wenfeng, 2016. "Disentangling the determinants of real oil prices," Energy Economics, Elsevier, vol. 56(C), pages 363-373.
    36. Cross, Jamie & Nguyen, Bao H., 2018. "Time varying macroeconomic effects of energy price shocks: A new measure for China," Energy Economics, Elsevier, vol. 73(C), pages 146-160.
    37. Chenghan Hou & Bao H. Nguyen, 2018. "Understanding the US natural gas market: A Markov switching VAR approach," CAMA Working Papers 2018-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
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    Keywords

    Oil prices; China; Vector autoregression (VAR); Markov-switching; Sign restrictions;
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