IDEAS home Printed from https://ideas.repec.org/a/ags/asagre/93238.html
   My bibliography  Save this article

Analysis and Forecast of Shaanxi GDP Based on the ARIMA Model

Author

Listed:
  • Ning, Wei
  • Kuan-jiang, Bian
  • Zhi-fa, Yuan

Abstract

Based on the 2008 Shaanxi Statistical Yearbook and the relevant data of Shaanxi GDP in the years 1952-2007, SPSS statistical software and time series analysis are used to establish ARIMA (1.2,1) time series model, according to the four steps, recognition rules and stationary test of time series under AIC criterion. ACF graph and PACF graph are used to conduct the applicability test on model. Then, the actual value and predicted value in the years 2002-2007 are compared in order to forecast the GDP of Shaanxi Province in the next 6 years based on this model. Result shows that the relative error of actual value and predicted value is within the range of 5%, and the forecasting effect of this model is relatively good. It is forecasted that the GDP of Shaanxi Province is 647.750, 765.662, 905.866, 10735.10, 12744.69 and 15158.20 billion yuan in the year from 2008 to 2013, respectively. According to the result, GDP of Shaanxi Province shoes a higher growth trend in the years 2008-2013. The forecasting result of this model is only a predicted value. But the national economy is a complex and dynamic system. We should pay attention to the risk of adjustment in economic operation and adjust the corresponding target value according to the actual situation.

Suggested Citation

  • Ning, Wei & Kuan-jiang, Bian & Zhi-fa, Yuan, 2010. "Analysis and Forecast of Shaanxi GDP Based on the ARIMA Model," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 2(01), pages 1-4, January.
    • Handle: RePEc:ags:asagre:93238
    DOI: 10.22004/ag.econ.93238
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/93238/files/____i.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.93238?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. Youssef, Jamile & Ishker, Nermeen & Fakhreddine, Nour, 2021. "GDP Forecast of the Biggest GCC Economies Using ARIMA," MPRA Paper 108912, University Library of Munich, Germany.
    2. Harris Ntantanis & Lawrence Pohlman, 2020. "Market implied GDP," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 636-646, December.
    3. Moahmed Hassan, Hisham & Haleeb, Amin, 2020. "Modelling GDP for Sudan using ARIMA," MPRA Paper 101207, University Library of Munich, Germany.

    More about this item

    Keywords

    Agribusiness;

    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:ags:asagre:93238. 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: AgEcon Search (email available below). General contact details of provider: .

    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.