预测中国GDP增长率:基于R语言和机器学习的分析  

Forecasting China’s GDP Growth Rate: An Analysis Based on R Language and Machine Learning

作　　者：左晨睿 

机构地区：[1]上海外国语大学国际金融贸易学院,上海

出　　处：《现代管理》2024年第4期830-844,共15页Modern Management

摘　　要：本文旨在通过运用R语言和机器学习技术,包括多元线性回归和随机森林模型,对中国的GDP增长率进行预测。研究探讨了GDP增长率对中国宏观经济政策和商业策略的重要性,进一步探讨了选取合适的预测模型。多元回归模型旨在探究各经济指标对GDP变动的影响,而随机森林模型则用于捕捉数据间的复杂非线性关系,并通过构建多个决策树提高预测的准确性。多元回归模型显示固定资产和工业是影响中国GDP增长率的显著因素,而CPI和贸易平衡的影响不显著。随机森林模型则强调了固定资产和工业在预测中国GDP增长率中的重要性。最后,论文指出了固定资产和工业对GDP增长的显著影响,并讨论了研究中存在的问题和未来的研究方向。This paper aims to forecast China’s GDP growth rate by using R language and machine learning techniques, including multiple linear regression and random forest model. This paper discusses the importance of GDP growth rate to China’s macroeconomic policy and business strategy, and further discusses the selection of appropriate forecasting models. The multiple regression model aims to explore the impact of various economic indicators on GDP changes, while the random forest model is used to capture complex nonlinear relationships between data and improve the accuracy of prediction by constructing multiple decision trees. The multiple regression model shows that fixed assets and industry are significant factors affecting China’s GDP growth rate, while CPI and trade balance have no significant effects. The random forest model emphasizes the importance of fixed assets and industry in predicting China’s GDP growth rate. Finally, the paper points out the significant impact of fixed assets and industry on GDP growth, and discusses the existing problems and future research directions.

关 键 词：中国GDP增长率 随机森林 多元回归模型 固定资产 机器学习模型 

分 类 号：F12[经济管理—世界经济]