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

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  • 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. Copyright (C) 2010 Blackwell Publishing Ltd.

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(s1), pages 499-519, August.
    • Handle: RePEc:bla:rdevec:v:14:y:2010:i:s1:p:499-519
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    Cited by:

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    2. Maria Jesus Herrerias and Eric Girardin, 2013. "Seasonal Patterns of Energy in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    3. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    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. Cun Zhang & Xiaojie Wang & Shangxin Fang & Xutao Shi, 2022. "Construction and Application of VR-AR Teaching System in Coal-Based Energy Education," Sustainability, MDPI, vol. 14(23), pages 1-14, December.
    6. 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).
    7. 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.
    8. Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    9. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    10. 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.

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