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Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach

Author

Listed:
  • Baomin Dong
  • Xuefeng Li
  • Boqiang Lin

Abstract

The paper studies the behavior of mid‐ to long‐run real coal price in the Chinese market. The problem is of great importance because the coal takes a 70% share in China's energy mix, and China is the world's second largest carbon emitter. An accurate forecast in coal price is crucial in predicting China's future energy consumption mix as well as the private sector's energy‐type‐related investment decisions. In estimation and forecasting, the shifting trend time‐series model suggested by Robert Pindyck is used to capture the technological progress that is unobservable to the econometrician. It is found that the shifting trend model with continuous and random changes in price level and trend outperforms plain vanilla ARIMA models. It is argued that the model postulated by Pindyck is robust even in a transition economy where energy prices are subject to relatively rigid regulatory control. Out‐of‐sample forecasts are provided.

Suggested Citation

  • Baomin Dong & Xuefeng Li & Boqiang Lin, 2010. "Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach," Review of Development Economics, Wiley Blackwell, vol. 14(3), pages 499-519, August.
    • Handle: RePEc:bla:rdevec:v:14:y:2010:i:3:p:499-519
    DOI: 10.1111/j.1467-9361.2010.00567.x
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    Cited by:

    1. Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    2. Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
    3. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    4. Bielak, Łukasz & Grzesiek, Aleksandra & Janczura, Joanna & Wyłomańska, Agnieszka, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Resources Policy, Elsevier, vol. 74(C).
    5. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    6. repec:aen:journl:ej34-2-06 is not listed on IDEAS
    7. Zhou, Wenji & Zhu, Bing & Chen, Dingjiang & Zhao, Fangxian & Fei, Weiyang, 2014. "How policy choice affects investment in low-carbon technology: The case of CO2 capture in indirect coal liquefaction in China," Energy, Elsevier, vol. 73(C), pages 670-679.
    8. Parviz Sohrabi & Behshad Jodeiri Shokri & Hesam Dehghani, 2023. "Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 207-216, June.
    9. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.

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