Establishment of Multivariate Linear and Logarithmic Function Mixed Model to Predict Economic Trend Based on 2005-2015 Data
- DOI
- 10.2991/icmete-19.2019.143How to use a DOI?
- Keywords
- Mixed model of multiple linear and logarithmic functions; eviews; Population size; Length of education Educational appropriations
- Abstract
According to the China Statistical Yearbook, the data of 2005-2015 were found. Eviews was used to establish a multiple linear regression model. The model did not pass the t test. Then a mixed model of multiple linear and logarithmic functions was established. The economic significance test, statistical test, econometric test and prediction test of the model were all passed, and the goodness of fit was close to 1. Secondly, we used this model to forecast the economic growth trend in the coming years. In the future, the decline of population growth rate (aging) would lead to the slowdown of economic growth. From the model, it can be seen that the economic growth rate was closely related to population growth. The downward trend of economic growth rate was irreversible and can only be delayed. Finally, from the perspective of the model, length of education was the second factor affecting economic development, so we suggested to the government: (1) Open the multi-child policy (or the three-child policy); (2) Implement general education to increase the length of education for all Reach to delay the downward trend of GDP.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Huimin Zou AU - Jiayi Wu PY - 2019/05 DA - 2019/05 TI - Establishment of Multivariate Linear and Logarithmic Function Mixed Model to Predict Economic Trend Based on 2005-2015 Data BT - Proceedings of the 2019 International Conference on Management, Education Technology and Economics (ICMETE 2019) PB - Atlantis Press SP - 600 EP - 607 SN - 2352-5428 UR - https://doi.org/10.2991/icmete-19.2019.143 DO - 10.2991/icmete-19.2019.143 ID - Zou2019/05 ER -