Author(s)

ShaSha Zhang

Affiliation(s)

School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, Guangxi, China.

Corresponding Author

ShaSha Zhang

ABSTRACT

This paper takes Henan Province as the research object, selects GDP as the index of regional economic vitality, and selects the index that affects GDP from the aspects of economic benefit, opening up, population, government regulation, residents' quality of life and enterprise vitality. Firstly, a multiple linear regression model is established to test the multicollinearity of the independent variables. Then, the ridge regression and LASSO regression models are established to correct them, and the multicollinearity problem between independent variables is solved. By comparing and analyzing the two models, the LASSO regression model is the optimal regression model. Secondly, the Holt exponential smoothing model is used to predict the time series of the five variables showing a linear trend in the LASSO regression equation. The simple exponential smoothing model is used to predict the four variables of random fluctuation, and the data of each variable in 2024 are predicted. The predicted value of GDP in Henan Province in 2024 is 61441.55 billion yuan. Finally, some suggestions are put forward for Henan Province from several aspects, so that the economic development of the region can form a virtuous circle, so as to enhance the competitiveness of its regional economy and promote the sustainable development of the economy.

KEYWORDS

Multivariate linear regression model; Ridge regression model; LASSO regression model; Time series prediction; Regional economic vitality

CITE THIS PAPER

ShaSha Zhang. Analysis and decision-making of regional economic vitality and its influencing factors. Trends in Social Sciences and Humanities Research. 2024, 2(4): 90-101. DOI: 10.61784/tsshr3016.

REFERENCES

[1] Hu Jinyana, Cai Yongfanga, Cheng Jiaminga. Evaluation Model of the Regional Economic Vitality. Journal of Physics: Conference Series, 2020(3).

[2] Zhao Dan, Zhao Zhi, Chen Zening. Construction of Regional Economic Vitality Model. Journal of Economic Science Research, 2020(2): 19-23.

[3] Xin Siqian, Xin Sihan, Hu Buen, Wang Penghui, Fang Kang. Analysis and Decision of Regional Economic Vitality and its Influencing Factors. Frontiers in Economics and Management, 2020(1): 76-85.

[4] Pang Jinfeng, He Jintao, Luo Lan, Li Yao, Mao Yiren, Zhang Hanrui. Analysis of China's Regional Economic Vitality and Its Influential Factors Based on System Cluster Model and Computer Multiple Regression Model. Journal of Physics: Conference Series, 2020.

[5] Zhong Zhenfang. Factors Influencing Regional Economic Vitality Based on Regression Analysis. E3S Web of Conferences, 2020.

[6] Liu Yi, You Xiaoyu, Zhang Chunshuo. Regional Economic Vitality Based on Weighted Grey Relational Analysis. Journal of Economic Science Research, 2020(2): 12-18.

[7] Gan Mingli. Empirical Analysis on Influencing Factors of Financial Revenue in Sichuan Province. Journal of Economics and Public Finance, 2022(8): 68-74.

[8] Zhang Enquan. Empirical analysis of the influencing factors of fiscal revenue in Xinjiang. Economic Forum, 2015(5): 26-30.

[9] Guo Lin, Guo Li, Zhang Na, Tang Biao. Empirical Analysis on the Influencing Factors of Fiscal Revenue in China. Modern Business Trade Industry, 2016(27): 135-136.

[10] Li Lvxiu, Dong Xiyuan. Empirical analysis based on Eviews on China’s fiscal revenue factors. China Collective Economy, 2021(16): 92-94.

[11] Xie Liying. Empirical Analysis on the Influencing Factors of Fiscal Revenue in Anhui Province. Modern Business Trade Industry, 2019(9): 115-116.

[12] You Jiawei. Empirical analysis of the influencing factors of China’s fiscal revenue. China Collective Economy, 2019(7): 100-103.

[13] McDonald Gary C. Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 2009(1): 93-100.

[14] Tibshirani Rob. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society. Series B: Methodological, 1996(58): 267-288.

[15] Zou H. The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, 2006(101): 1418-1429.

[16] Nie Ruichao. Analysis of influencing factors of Fiscal revenue in Beijing based on Ridge regression and Lasso regression model. International Journal of New Developments in Engineering and Society, 2022(6): 1-5.