Proceedings of the 7th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2021)

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Modeling and Forecasting Gross Domestic Product in Different Regions of China

Authors
Lingyun Duan1, 2, 3, 4, 5, Ziyuan Liu1, 2, 3, 4, Wen Yu1, 2, 3, 4, 5, *, Wei Chen1, 2, 3, 4, Dongyan Jin1, 2, 3, 4, Jiajia Liu1, 2, 3, 4, Han Zhou1, 2, 3, 4, Suhua Sun1, 2, 3, 4, 5, Ruixi Dai1, 2, 3, 4, 5
1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2Key Laboratory of Agricultural Information Service Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China
3Key Laboratory of Intelligent Agricultural Early Warning Technology and System, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
4Beijing Engineering Research Center for Agricultural Monitoring and Early Warning, Beijing 100081, P.R.China
5Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
*Corresponding author. Email: yuwen@caas.cn
Corresponding Author
Wen Yu
Available Online 15 December 2021.
DOI
10.2991/assehr.k.211215.055How to use a DOI?
Keywords
GDP; ECM; Cointegration test; ARIMA
Abstract

Gross Domestic Product (GDP) is an important indicator to measure a country’s economic operation in a certain period. By understanding the speed and quality of economic development, it plays a guiding role in policy formulation and infrastructure planning. This article selects sample data of fixed asset investment in various regions from 1990 to 2017, per capita years of education in each region from 1990 to 2017, and GDP of each region from 1990 to 2019 to predict the GDP of each region from 2020 to 2050. The modeling ideas are as follows: firstly, through the cointegration test, it is judged that the regional GDP model with fixed asset investment and per capita education years as explanatory variables has a long-term equilibrium relationship. Secondly, the ECM is established, and the ARIMA model is used to fit the predictive values of the explanatory variables. Finally, the predictive value of the explanatory variables is used to calculate the GDP of each region. Result: the GDP of all regions is on the rise, and GDP maintains stable and rapid development; the average years of education per capita has become an influencing factor for economic growth, and investment in fixed assets is still an important means of stimulating the economy. Conclusion: the government should properly adjust the investment structure of fixed assets, cultivate scientific and technological innovation talents, develop knowledge-based economy and promote better economic development.

Copyright
© 2021 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 7th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
15 December 2021
ISBN
10.2991/assehr.k.211215.055
ISSN
2352-5398
DOI
10.2991/assehr.k.211215.055How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Lingyun Duan
AU  - Ziyuan Liu
AU  - Wen Yu
AU  - Wei Chen
AU  - Dongyan Jin
AU  - Jiajia Liu
AU  - Han Zhou
AU  - Suhua Sun
AU  - Ruixi Dai
PY  - 2021
DA  - 2021/12/15
TI  - Modeling and Forecasting Gross Domestic Product in Different Regions of China
BT  - Proceedings of the 7th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2021)
PB  - Atlantis Press
SP  - 300
EP  - 304
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.211215.055
DO  - 10.2991/assehr.k.211215.055
ID  - Duan2021
ER  -