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Analysis and Forecast of Shaanxi GDP Based on the ARIMA Model

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  • 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
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    Cited by:

    1. Moahmed Hassan, Hisham & Haleeb, Amin, 2020. "Modelling GDP for Sudan using ARIMA," MPRA Paper 101207, University Library of Munich, Germany.
    2. Youssef, Jamile & Ishker, Nermeen & Fakhreddine, Nour, 2021. "GDP Forecast of the Biggest GCC Economies Using ARIMA," MPRA Paper 108912, University Library of Munich, Germany.
    3. Harris Ntantanis & Lawrence Pohlman, 2020. "Market implied GDP," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 636-646, December.

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    Keywords

    Agribusiness;

    Statistics

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