The Frontiers of Society, Science and Technology, 2020, 2(14); doi: 10.25236/FSST.2020.021409.
Tu Guangyu
School of Software, Nanchang University, Nanchang, 330047, China
We first use linear regression model to study the relationship between regional economic vitality and related factors. However, the model fails the variance inflation factor (VIF) test, so we established a grey relational analysis (GRA) model. Next, we selected Zhengzhou as the research object then established a time series and autoregressive integrated moving average (ARIMA) model to predict the GDP of Zhengzhou after the "Twelfth Five-Year Plan" economic transformation and the long-term impact of economic policy transformation on Zhengzhou. Finally, we use the entropy method to determine the weight of indicators we collect and rank some cities. Based on those weight, previous analysis and models, we put forward development suggestions for Zhengzhou to enhance regional economic vitality.
VIF test, GRA, Time Series, ARIMA, Entropy Method
Jolene Qiu. Analysis and Decision-making of Regional Economic Vitality and Its Influencing Factors. The Frontiers of Society, Science and Technology (2020) Vol. 2 Issue 14: 50-73. https://doi.org/10.25236/FSST.2020.021409.
[1] Jiang Qiyuan. Mathematics models. Beijing: High Education Press, May 2018, page 331-335.
[2] Shi Meijuan. Application of ARIMA Model in Forecasting the Investment of Fixed Assets in Shanghai [J]. Mathematical Statistics and Management, 2005
[3] You Daming. XuFei. Evaluation Model of Urban Sustainable Development Based on Entropy [J]. Journal of Xiamen University; Philosophy and Social Science Edition, 2004 (2): 109-115
[4] National Bureau of Statistics. China Statistical Yearbook: 2004 [M]. Beijing;China Statistics Press, 2005
[5] Samuelson P A. Paul Douglas’s measurement of production functions and marginal productivity[J]. Journal of Polital Economy.1999,87:923-939