Model and forecast for China's GDP based on combined ARIMA and 
linear regression model

Jing Xie; Wei Zhang; Tianyue Zhou

doi:10.1117/12.2628070

22 April 2022 Model and forecast for China's GDP based on combined ARIMA and linear regression model

Jing Xie, Wei Zhang, Tianyue Zhou

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121632D (2022) https://doi.org/10.1117/12.2628070
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

In this work, a combination of models is used to forecast the GDP of China and analyze the possible parameters that affect the GDP of China. The combined model includes linear regression model to establish relation between the GDP and possible significant parameters that affect the GDP and Autoregressive Integrated Moving Average model (ARIMA) to estimate and forecast those significant parameters. Consequently, these predicted parameters were brought in previous linear regression model, established a new model to forecast the GDP. After that, the new model was compared with the ARIMA model, then the probably more suitable model was used to predict the GDP of China in the future. The result of this work is that multiple linear regression model combined with the ARIMA model is more suitable in predicting the GDP of China. The significance of this work is that it states a new method which combined two models, and the new model is probably more suitable than the previous methods. As a result, other studies about GDP of China can consider this new method.

Citation Download Citation

Jing Xie, Wei Zhang, and Tianyue Zhou "Model and forecast for China's GDP based on combined ARIMA and linear regression model", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121632D (22 April 2022); https://doi.org/10.1117/12.2628070

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