IDEAS home Printed from https://ideas.repec.org/a/bla/growch/v49y2018i4p712-742.html
   My bibliography  Save this article

Forecasting China's industrial output using a spatial Bayesian vector autoregressive model

Author

Listed:
  • Donald J. Lacombe
  • Nyakundi M. Michieka

Abstract

This paper forecasts cement, steel, and TV production in China's top industrial provinces using a Bayesian prior that incorporates both time and spatial dependence as proposed by LeSage and Cashell (2015). Results indicate that growth in cement production will increase following the 3‐year slump experienced between 2013 and 2016 in the five provinces in our sample. Average growth rates for steel production between 2017 and 2018 are similar to those experienced in 1999 and 2008. Our findings indicate that forecast accuracy for TV production demonstrate the superior forecasting characteristics of the hybrid prior.

Suggested Citation

  • Donald J. Lacombe & Nyakundi M. Michieka, 2018. "Forecasting China's industrial output using a spatial Bayesian vector autoregressive model," Growth and Change, Wiley Blackwell, vol. 49(4), pages 712-742, December.
    • Handle: RePEc:bla:growch:v:49:y:2018:i:4:p:712-742
    DOI: 10.1111/grow.12251
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/grow.12251
    Download Restriction: no
    File URL: https://libkey.io/10.1111/grow.12251?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
    ---><---

    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:growch:v:49:y:2018:i:4:p:712-742. 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=0017-4815 .

    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.