Influencing factors analysis of China's iron import price: Based on quantile regression model. (June 2016)

• Record Type: Journal Article
• Authors: Chen, Wenhui; Lei, Yalin; Jiang, Yong
• Is Part Of: Resources policy. Volume 48(2016)
• Page Start: 68
• Page End: 76
• Publication Date: 2016-06
• Subjects: Iron ore prices -- Impact factors -- Quantile regression -- Polynomial distributed lag model -- Policy recommendations; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Ressources naturelles -- Gestion -- Périodiques; Environnement -- Politique gouvernementale -- Périodiques; 333.8
• DOI: 10.1016/j.resourpol.2016.02.007
• Languages: English
• ISSNs: 0301-4207

Abstract

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. Highlights: Influencing factors of iron import price are analyzed under different quantile. Stepwise regression method is used to select significant variables. The effect intensity of the factors are various under the different quantile. Demand has the greatest positive impact on prices.