Economic Forecasting Based on Time Series Analysis

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)

Abstract

To analyze and formulate economic development target and policy, predicting the future value of China’s per capita GDP is very important. The economic time series will be pretreated for constructing prediction model by statistical software SPSS and Views through exponential smoothing method and ARIMA model. The conclusion is that China’s per capita GDP from 2012 to 2016 was predicted. Statistical results show that China’s per capita GDP is steadily improving.

Keywords

Per capita GDP Exponential smoothing method ARIMA model 

References

  1. 1.
    Wang J (2004) Statistics case for teaching, vol 9(4). Statistics Press, Beijing, pp 325–354Google Scholar
  2. 2.
    Su J (2011) Application of statistics software SPSS on Stock market. Stat Obs 5(6):9–15Google Scholar
  3. 3.
    Shi M (2010) Application of ARIMA on investment in fixed assets in Shanghai. Stat Obs 3(7):7–12Google Scholar
  4. 4.
    Zhu L (2011) Application of ARIMA model on stock predicting. Jiangsu Statistics 3(1):17–23Google Scholar
  5. 5.
    Li G (2011) Comparison of methods for estimating based on ARIMA model. Appl Probab Stat 2:23–25Google Scholar
  6. 6.
    Altman EI (1968) Financial ratios, discriminate analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609CrossRefGoogle Scholar
  7. 7.
    Higgins RC (2010) Analysis for financial management. Sci Cult Publ House (H.K) 8(4):125–127Google Scholar
  8. 8.
    Liu GY (2007) Vulnerability indicator a work in progress, early warning system models: the next steps forward. IMF Official Website 4(6):123–134Google Scholar
  9. 9.
    Zhang X (2009) Econometric analysis, vol 5(9). Statistics Press, Beijing, pp 124–128Google Scholar
  10. 10.
    Chen Yaohui (2011) Parameter estimation and prediction with ARIMA model. Syst Eng 3(5):6–12Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of International tradeZhengzhou Shengda College of Economics and Trade ManagementZhengzhouChina

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