IDEAS home Printed from https://ideas.repec.org/a/bla/rdevec/v14y2010i3p499-519.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9361.2010.00567.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9361.2010.00567.x?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
    ---><---

    Citations

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


    Cited by:

    1. 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).
    2. 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).
    3. 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.

    More about this item

    Statistics

    Access and download statistics

    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:bla:rdevec:v:14:y:2010:i:3:p:499-519. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1363-6669 .

    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.