Research of Forecasting Chinese Gross Domestic Product Based on Auto Regressive Integrated Moving Average Algorithm and Linear Regression Model

An Zhang

doi:10.54254/2754-1169/101/20231594

Research Article

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Published on 25 July 2024

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Zhang,A. (2024). Research of Forecasting Chinese Gross Domestic Product Based on Auto Regressive Integrated Moving Average Algorithm and Linear Regression Model. Advances in Economics, Management and Political Sciences,101,151-160.

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Research of Forecasting Chinese Gross Domestic Product Based on Auto Regressive Integrated Moving Average Algorithm and Linear Regression Model

An Zhang *^,1,

¹ University of California, Davis, Davis, California,95617, United States

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/101/20231594

Abstract

Gross domestic product (GDP) is an important indicator for measuring a country's economic development level. By studying GDP, it can analyze the economic structure of a country or region, determine the development trend of a country. GDP is widely applied in fields such as macroeconomic analysis, policy formulation, and international economic comparison. After Chinese reform and opening up, the economy has developed rapidly, and the GDP has shown a trend of increasing year by year. Therefore, the analysis of Chinese GDP is meaningful. This article analyzes GDP data from 2001 to 2017. Linear regression method is used for prediction first but the goodness of fit for this model to predict GDP is not very high. So then the Auto Regressive Integrated Moving Average (ARIMA) model is used for prediction. The results indicate that the ARIMA algorithm has higher goodness of fit and can better illustrate the situation, which can be used for short-term prediction in China. All this model is realized in the python.

Keywords

GDP, ARIMA, Forecasting, Time Series

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References

[1]. Yu Lianmin, The Application of ARIMA Model in China's GDP Forecast, Times Finance, no. 21, pp. 1, 2017

[2]. Rawski, T. G. (2001). What is happening to China’s GDP statistics?, China Economic Review, 12(4), 347–354. doi:10.1016/s1043-951x(01)00062-1

[3]. Chen, M., Kwok, C. L., Shan, H., & Yip, P. S. F. (2018). Decomposing and Predicting China’s GDP Growth: Past, Present, and Future, Population and Development Review, 44(1), 143–157. doi:10.1111/padr.12129

[4]. Asif Mahmud, Ian Hamilton, Vikash V. Gayah, Richard J. Porter, Estimation of VMT using heteroskedastic log-linear regression models, Transportation Letters, 2023, ISSN 1942-7867,

[5]. Chun-Hua Chien, Amy J.C. Trappey, Chien-Chih Wang, ARIMA-AdaBoost hybrid approach for product quality prediction in advanced transformer manufacturing, Advanced Engineering Informatics, Volume 57, 2023, 102055, ISSN 1474-0346

[6]. HHXUN, ARIMA model introduction, 2018-04-08 21:52:10

[7]. Jun Luo, Yaping Gong, Air pollutant prediction based on ARIMA-WOA-LSTM model, Atmospheric Pollution Research,Volume 14, Issue 6,2023,101761,ISSN 1309-1042,

[8]. Douglas C. Montgomery, Elizabeth A. Peck,G. Geoffrey Vining, Introduction to linear regression analysis, pp. 13 , 2021

[9]. Draper, N.R. and Smith, H. Applied Regression Analysis. Wiley Series in Probability and Statistics. 1998.

[10]. S.L. Ho and M. XIE, THE USE OF ARIMA MODELS FOR RELIABILITY FORECASTING AND ANALYSIS, Computers & industrial engineering, 1998 - Elsevier

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Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Conference website: https://2023.icftba.org/

ISBN：978-1-83558-525-2(Print) / 978-1-83558-526-9(Online)

Conference date: 8 November 2023

Editor：Javier Cifuentes-Faura

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.101

ISSN：2754-1169(Print) / 2754-1177(Online)

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