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Cointegration and causal relationships among steel prices of Mainland China, Taiwan, and USA in the presence of multiple structural changes

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  • Chien-Chung Nieh
  • Hwey-Yun Yau
  • Ken Hung
  • Hong-Kou Ou
  • Shine Hung

Abstract

Variation in the price of steel is an important factor to take into consideration when discussing cost control and management decisions in the construction industry. We employ various conventional and advanced econometrics methods to examine the interrelationships of steel prices in three related markets during the time period June 2002 to May 2010: Mainland China (CH), Taiwan (TW), and the United States (US). We adopt the Gregory and Hansen (GH) test and regime-switching (RS) model for cointegration, both of which accommodate endogenous structural break(s), to produce a more accurate analysis of a period in the presence of structural change(s). The empirical result of the RS cointegration test with respect to multiple structural breaks suggests a long-run equilibrium relationship among the three variables considered. This finding differs from the result of the GH test but confirms the result of the conventional Johansen test. Furthermore, the results of the Granger causality test indicate that both CH and US steel prices have great influence on the TW steel price; the Taiwanese steel market is closely linked with China and US steel markets in the long run. Copyright Springer-Verlag 2013

Suggested Citation

  • Chien-Chung Nieh & Hwey-Yun Yau & Ken Hung & Hong-Kou Ou & Shine Hung, 2013. "Cointegration and causal relationships among steel prices of Mainland China, Taiwan, and USA in the presence of multiple structural changes," Empirical Economics, Springer, vol. 44(2), pages 545-561, April.
    • Handle: RePEc:spr:empeco:v:44:y:2013:i:2:p:545-561
    DOI: 10.1007/s00181-012-0556-6
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    Cited by:

    1. 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.
    2. Guo, Shaopeng & Zhao, Jun & Wang, Weilong & Yan, Jinyue & Jin, Guang & Zhang, Zhiyu & Gu, Jie & Niu, Yonghong, 2016. "Numerical study of the improvement of an indirect contact mobilized thermal energy storage container," Applied Energy, Elsevier, vol. 161(C), pages 476-486.
    3. Guo, Sui & Li, Huajiao & An, Haizhong & Sun, Qingru & Hao, Xiaoqing & Liu, Yanxin, 2019. "Steel product prices transmission activities in the midstream industrial chain and global markets," Resources Policy, Elsevier, vol. 60(C), pages 56-71.
    4. Qi, Yajie & Li, Huajiao & Liu, Yanxin & Feng, Sida & Li, Yang & Guo, Sui, 2020. "Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective," Resources Policy, Elsevier, vol. 67(C).

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    More about this item

    Keywords

    Steel price; Cointegration; Structural change; Granger causality; Construction management; C22; F15; F36;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F15 - International Economics - - Trade - - - Economic Integration
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration

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