Forecdiction of GDP Appreciation Ratio based on
ARMA Model and Multiple Regression

Zhi Zhang; Hang Zhang; Xiang Han

doi:https://doi.org/10.14445/22315373/IJMTT-V69I7P503

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 69 | Issue 7 | Year 2023 | Article Id. IJMTT-V69I7P503 | DOI : https://doi.org/10.14445/22315373/IJMTT-V69I7P503

Forecdiction of GDP Appreciation Ratio based on ARMA Model and Multiple Regression

Zhi Zhang, Hang Zhang, Xiang Han

Received	Revised	Accepted	Published
30 May 2023	03 Jul 2023	15 Jul 2023	28 Jul 2023

Citation :

Zhi Zhang, Hang Zhang, Xiang Han, "Forecdiction of GDP Appreciation Ratio based on ARMA Model and Multiple Regression," International Journal of Mathematics Trends and Technology (IJMTT), vol. 69, no. 7, pp. 17-31, 2023. Crossref, https://doi.org/10.14445/22315373/IJMTT-V69I7P503

Abstract

This paper selects the national GDP data from 2000 to 2021, using the method of multiple linear regression and ARMA model, analyze and forecast the national GDP value-added ratio, select the eight GDP value ratio influencing factors, the empirical analysis is conducted on the influencing factors of the GDP increment ratio through gradual regression, find out the influencing factors significantly affect the GDP value-added ratio. On this basis, we found a long-term stable relationship between the GDP value-added ratio and the related influencing variables through the co-integration test. By fitting the ARMA model in R language, we finally obtained the forecast value of GDP appreciation ratio in the next five years, analyzed the change trend, and combined with the current situation of the influencing factors of GDP increment ratio, analyze the future development of various industries.

Keywords

Multiple linear regression, Stepwise regression, ARMA model, Co-integration test.

References

[1] R.I. Jennrich, and P.F. Sampson, “Application of Stepwise Regression to Non-linear Estimation,” Technometrics, vol. 10, no. 1, pp. 63- 72, 1968.
[Google Scholar] [Publisher Link]
[2] H.L. Gray, G.D. Kelley, and D.D. Mc Intire, “A New Approach to ARMA Modeling,” Communications in Statistics-Simulation and Computation, vol. 7, no. 1, pp. 1-77, 1978.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Leland Wilkinson, “Tests of Significance in Stepwise Regression,” Psychological Bulletin, vol. 86, no. 1, pp. 168-174, 1979.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Elias Samaras et al., “AARMA Representation of Random Processes,” Journal of Engineering Mechanics, vol. 111, no. 3, 1985.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ludwig Fahrmeir, and Gerhard Tutz, Multivariate Statistical Modelling Based on Generalized Linear Models, New York: Springer-Verlag, 1994.
[Google Scholar]
[6] Jin-Li Hu, and Cheng-Hsun Lin, “Disaggregated Energy Consumption and GDP in Taiwan: A Threshold Co-integration Analysis,” Energy Economics, vol. 30, no. 5, pp. 2342-2358, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Colin M. Beale et al., “Regression Analysis of Spatial Data,” Ecology Letters, vol. 13, no. 2, pp. 246-264, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[8] John Fox, and Sanford Weisberg, “Multivariate Linear Models in R, An R Companion to Applied Regression,” Los Angeles: Thousand Oaks, 2011.
[Google Scholar]
[9] Byoung Seon Choi, ARMA Model Identification, Springer Science & Business Media, 2012.
[Google Scholar] [Publisher Link]
[10] Nelson Fumo, and M.A. Rafe Biswas, “Regression Analysis for Prediction of Residential Energy Consumption,” Renewable and Sustainable Energy Reviews, vol. 47, pp. 332-343, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] L. L, “Application of Time Series Analysis in GDP Prediction in China,” Time Finance, vol. 670, no. 24, pp. 20-24, 2017.
[12] H.R. Zhao, “Measurement Analysis of Factors Influencing GDP in China,” China Collective Economy, vol. 686, no. 30, no. 12-14, 2021.
[13] Huanyu Zheng, Malin Song, and Zhiyang Shen, “The Evolution of Renewable Energy and Its Impact on Carbon Reduction in China,” Energy, vol. 237, p. 121639, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Chunmiao Jia et al., “Prediction of Railway Freight Volume in Ganning District based on ARMA Model and Multiple Regression,” Integrated Transportation, vol. 44, no. 9, pp. 147-154, 2022.
[Google Scholar]