Chinese GDP Forecast Using ARIMA Model

Fawaz Hamood Abdulazis Abdol Ali

Abstract


China's economy is very interesting to analyze because it is recognized as the highest GDP inthe world. Despite the ability of China's economy to reform and grow, China shows fluctuationin its economy especially after the crisis in 1997 and 2008. When China was able to counterthe 2008 financial crisis, unfortunately starting from 2010 the GDP growth started to decreaseagain. Therefore, the objective of this research is to analyze the GDP of China in twoconsecutive years of 2016 and 2017 using the ARIMA. The journal that will be used uses atime series. time series is commonly used for series of data obtained chronologically. Thefuture value of a time series can most likely be predicted through its current and past values.This research uses EViews software. Eviews software can be called a combination of softwarespecifically made to process data on time series. This research also uses the ModelAutoregressive Integrated Moving Average (ARIMA) method, a time series estimationmethod, which can be used with EViews software. Based on the EViews software, theforecasting process with the ARIMA model is illustrated in this work, namely, China's GrossDomestic Product (GDP) estimated from 2016 to 2018.

Full Text:

PDF

References


X.T. Zhang. A Guide to Using EViews. China Machine Press, Beijing, 2007.

D.W. Zhang, B. Liu, Q. Liu. Eviews Data Statistics and Analysis Tutorial. Tsinghua University Press, Beijing, 2010.

G.E.P. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung. Time Series Analysis: Forecasting and Control. 5th ed. Wiley, New York, 2015.

H.H. Fan, L.Y. Zhang. EViews Statistical Analysis and Application. China Machine Press, Beijing, 2009.

Information on https://ww2.mathworks.cn/help/econ/arima-model.html

L. Li. Application research of EViews software in ARIMA model. Journal of Anhui Vocational College of Electronics & Information Technology, 53 (2011) 31-32, 51.

D.M. Xue. Application of the ARIMA model in time series analysis. Journal of Jilin Institute of Chemical Technology. 27 (2010) 80-83.

C.C. Zhao, Z.Y. Shang. Application of ARMA Model on prediction of Per Capita GDP in Chengdu City. Ludong University Journal (Natural Science Edition). 28 (2012) 223- 226.

L. Zhang. Time series model and forecast of GDP per capita in Tianjin. Northern Economy. 3 (2007) 44-46.

Zitelmann, Rainer. (2019). State Capitalism? No, The Private Sector Was And Is The Main Driver Of China’s Economic Growth. https://www.forbes.com/sites/rainerzitelmann/2019/09/30/state-capitalism-no-the- private-sector-was-and-is-the-main-driver-of-chinas-economic- growth/?sh=2aaa867f27cb

Congressional Research Service. (2019). China’s Economic Rise: History, Trends, Challenges, and Implications for the United States. https://csrreports.congress.gov

Ma, L., Hu, C., Lin, R., & Han, Y. (2018). Arima model forecast based on eviews software. IOP Conference Series: Earth and Environmental Science, 208, 012017. https://doi.org/10.1088/1755-1315/208/1/012017

Mahmoud M. Abdelwahab, Atef F. Hashem, Hatem E. Semary. "ARIMA MODELS TO FORECAST COVID-19 IN KINGDOM OF SAUDI ARABIA DURING THE INTERVAL (1-2021 TO 1-2022) WEEKLY USING E-VIEWS PROGRAM", Advances and Applications in Statistics, 2022

Liu, Y., Chen, X., Zhu, L., & Zhou, Z. (2022). Forecast and analysis of national GDP in China based on Arima model. Academic Journal of Science and Technology, 3(2), 78– 83.

https://doi.org/10.54097/ajst.v3i2.2096

http://www.iep.ru

http://www.dscasc.edu.in

http://www.hausarbeiten.de

http://repository.its.ac.id




DOI: https://doi.org/10.24114/qej.v12i1.42058

Article Metrics

Abstract view : 177 times
PDF - 176 times

Refbacks

  • There are currently no refbacks.


Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

slot gacor slot