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arXiv 2407.03595econ.GNcs.LGq-fin.EC
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用于经济预测的机器学习：在中国GDP增长中的应用

Machine Learning for Economic Forecasting: An Application to China's GDP Growth

Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）

Yanqing Yang, Xingcheng Xu, Jinfeng Ge, Yan Xu

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AI总结：

本文应用多种机器学习模型预测中国季度实际GDP增长，发现其平均预测误差通常低于传统计量模型和专家预测，但在经济拐点处专家预测有时更准确，并通过可解释机器学习方法识别影响宏观经济波动的关键变量。

摘要：

本文旨在探索机器学习在中国宏观经济变量预测中的应用。具体而言，本文使用多种机器学习模型预测中国季度实际GDP增长，并分析导致这些模型表现差异的因素。研究发现，机器学习模型的平均预测误差通常低于传统计量经济学模型或专家预测，尤其是在经济稳定时期。然而，在某些拐点时期，尽管机器学习模型仍优于传统计量经济学模型，但专家预测在某些情况下可能表现出更高的准确性，这是因为专家对宏观经济环境和实时经济变量有更全面的理解。除宏观经济预测外，本文还采用可解释机器学习方法识别不同机器学习模型中的关键归因变量，旨在增强对其在宏观经济波动中贡献的理解和评估。

英文摘要：

This paper aims to explore the application of machine learning in forecasting Chinese macroeconomic variables. Specifically, it employs various machine learning models to predict the quarterly real GDP growth of China, and analyzes the factors contributing to the performance differences among these models. Our findings indicate that the average forecast errors of machine learning models are generally lower than those of traditional econometric models or expert forecasts, particularly in periods of economic stability. However, during certain inflection points, although machine learning models still outperform traditional econometric models, expert forecasts may exhibit greater accuracy in some instances due to experts' more comprehensive understanding of the macroeconomic environment and real-time economic variables. In addition to macroeconomic forecasting, this paper employs interpretable machine learning methods to identify the key attributive variables from different machine learning models, aiming to enhance the understanding and evaluation of their contributions to macroeconomic fluctuations.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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