IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v48y2016icp68-76.html
   My bibliography  Save this article

Influencing factors analysis of China’s iron import price: Based on quantile regression model

Author

Listed:
  • Chen, Wenhui
  • Lei, Yalin
  • Jiang, Yong

Abstract

When encountering high import prices and price volatility, China does not have the power to affect prices, although China has ranked first in iron ore imports since 2003. The existing literature usually investigates the impact factors of iron ore prices using the averaging method. It is difficult to depict the detailed impact of various factors on prices accurately. To provide sounder basis for the Chinese government to enact policy, this paper develops a quantile regression model with the lagged variables to measure factors that affect the import prices of iron ore in China under high, medium and low price levels. The analysis uses monthly data through January 2003 to March 2015. The results indicate that the effect intensity of the factors on the prices are various under different quantiles. As prices rise, the degree of positive influence of previous period of crude steel production on iron ore prices is gradually decreasing; conversely, the strength of previous period of import volume’s negative effect on prices is falling. Furthermore, it verifies that China has no voice in the international iron ore market. In low quantile, the strength of effect of prior period iron ore volume on prices is higher than that of China’s production of iron ore on import prices because the grade of China's iron ore resources is low. Therefore, when the iron ore prices are at a low quantile, China should expand the import of iron ore appropriately and reduce the exploitation of low-grade iron ore resources. Additionally, China should optimize crude steel output and actively invest in overseas iron ore exploration and mining to reduce the effect of prices fluctuations by reducing the dependence on imported iron ore. China may also promote the development of an international iron ore futures market and innovate iron ore business models to hedge foreign exchange risks because of the US dollar index has greatest negative effect on the prices.

Suggested Citation

  • Chen, Wenhui & Lei, Yalin & Jiang, Yong, 2016. "Influencing factors analysis of China’s iron import price: Based on quantile regression model," Resources Policy, Elsevier, vol. 48(C), pages 68-76.
    • Handle: RePEc:eee:jrpoli:v:48:y:2016:i:c:p:68-76
    DOI: 10.1016/j.resourpol.2016.02.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420716300125
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2016.02.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wilson, Jeffrey D., 2012. "Chinese resource security policies and the restructuring of the Asia-Pacific iron ore market," Resources Policy, Elsevier, vol. 37(3), pages 331-339.
    2. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    3. Sukagawa, Paul, 2010. "Is iron ore priced as a commodity? Past and current practice," Resources Policy, Elsevier, vol. 35(1), pages 54-63, March.
    4. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    5. Tcha, MoonJoong & Wright, Damione, 1999. "Determinants of China's import demand for Australia's iron ore," Resources Policy, Elsevier, vol. 25(3), pages 143-149, September.
    6. Pustov, Alexander & Malanichev, Alexander & Khobotilov, Ilya, 2013. "Long-term iron ore price modeling: Marginal costs vs. incentive price," Resources Policy, Elsevier, vol. 38(4), pages 558-567.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Chang, Hui-Shung, 1994. "Estimating Japanese import shares of iron ore," Resources Policy, Elsevier, vol. 20(2), pages 87-93, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jie Zhang & Lu Zhang, 2016. "Impacts on CO 2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme," Sustainability, MDPI, vol. 8(11), pages 1-12, November.
    2. Su, Chi-Wei & Wang, Kai-Hua & Chang, Hsu-Ling & Dumitrescu–Peculea, Adelina, 2017. "Do iron ore price bubbles occur?," Resources Policy, Elsevier, vol. 53(C), pages 340-346.
    3. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    4. Qiangfeng, Li & Weiqiong, Zhong & Gaoshang, Wang & Jinhua, Cheng & Tao, Dai & Bojie, Wen & Liang, Liang & Qindong, Yang, 2018. "Material and value flows of iron in Chinese international trade from 2010 to 2016," Resources Policy, Elsevier, vol. 59(C), pages 139-147.
    5. Yufeng CHEN & Shuo YANG, 2022. "How Does the Reform in Pricing Mechanism Affect the World’s Iron Ore Price: A Time-Varying Parameter SVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 83-103, April.
    6. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    7. Zhu, Xuehong & Zheng, Weihang & Zhang, Hongwei & Guo, Yaoqi, 2019. "Time-varying international market power for the Chinese iron ore markets," Resources Policy, Elsevier, vol. 64(C).
    8. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Feng, Sida & Guo, Sui, 2019. "The impact of Chinese steel product prices based on the midstream industry chain," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    9. Wei, Jiangqiao & Ma, Zhe & Wang, Anjian & Li, Pengyuan & Sun, Xiaoyan & Yuan, Xiaojing & Hao, Hongchang & Jia, Hongxiang, 2022. "Multiscale nonlinear Granger causality and time-varying effect analysis of the relationship between iron ore futures and spot prices," Resources Policy, Elsevier, vol. 77(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Su, Chi-Wei & Wang, Kai-Hua & Chang, Hsu-Ling & Dumitrescu–Peculea, Adelina, 2017. "Do iron ore price bubbles occur?," Resources Policy, Elsevier, vol. 53(C), pages 340-346.
    2. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
    3. Germeshausen, Robert & Panke, Timo & Wetzel, Heike, 2014. "Investigating the influence of firm characteristics on the ability to exercise market power: A stochastic frontier analysis approach with an application to the iron ore market," ZEW Discussion Papers 14-105, ZEW - Leibniz Centre for European Economic Research.
    4. Sun, Sizhong & Anwar, Sajid, 2019. "R&D activities and FDI in China’s iron ore mining industry," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 47-56.
    5. Yufeng CHEN & Shuo YANG, 2022. "How Does the Reform in Pricing Mechanism Affect the World’s Iron Ore Price: A Time-Varying Parameter SVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 83-103, April.
    6. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    7. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    8. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    9. Paulo M.M. Rodrigues & Rita Fradique Lourenço, 2015. "House prices: bubbles, exuberance or something else? Evidence from euro area countries," Working Papers w201517, Banco de Portugal, Economics and Research Department.
    10. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    11. Yayan Hernuryadin & Koji Kotani & Tatsuyoshi Saijo, 2020. "Time Preferences of Food Producers: Does “Cultivate and Grow” Matter?," Land Economics, University of Wisconsin Press, vol. 96(1), pages 132-148.
    12. Klomp, Jeroen, 2013. "Government interventions and default risk: Does one size fit all?," Journal of Financial Stability, Elsevier, vol. 9(4), pages 641-653.
    13. Bampinas, Georgios & Panagiotidis, Theodore, 2016. "Hedging inflation with individual US stocks: A long-run portfolio analysis," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 374-392.
    14. Strike Mbulawa & Francis Nathan Okurut & Mogale Ntsosa & Narain Sinha, 2020. "Dynamics of Corporate Dividend Policy under Hyperinflation and Dollarization: A Quantile Regression Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 13(3), pages 70-82, December.
    15. Jean-Marc Fournier & Isabell Koske, 2012. "The determinants of earnings inequality: evidence from quantile regressions," OECD Journal: Economic Studies, OECD Publishing, vol. 2012(1), pages 7-36.
    16. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    17. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    18. Holmes, Mark J. & Otero, Jesús & Panagiotidis, Theodore, 2013. "On the dynamics of gasoline market integration in the United States: Evidence from a pair-wise approach," Energy Economics, Elsevier, vol. 36(C), pages 503-510.
    19. Dimic, Nebojsa & Piljak, Vanja & Swinkels, Laurens & Vulanovic, Milos, 2021. "The structure and degree of dependence in government bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    20. Osuji E. & Evans O., 2020. "Tourism Effects of Pandemics: New Insights from Novel Coronavirus," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 70(3-4), pages 56-65, July-Dece.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:48:y:2016:i:c:p:68-76. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.