摘要
机器学习方法在处理复杂数据、构建高精度模型方面具有显著优势,在不同领域的研究中都得到了广泛应用。数字经济时代为经济领域带来海量数据的同时,也对经济研究提出诸多挑战。机器学习方法能充分挖掘数据中的非线性、非平稳信息,有效提高经济分析结果的精度。机器学习与经济问题的融合改变了传统经济学的研究范式。文章对近年来机器学习方法在经济研究中的应用进行回顾,从通货膨胀、汇率与货币、GDP、劳动力市场、社会稳定、政策评价等角度进行总结,比较了常用机器学习方法的优缺点,并展示了模型的评价准则,如均方根误差、F1-得分、AUC值等。
Machine learning methods have obvious advantages in processing complex data and constructing high-precision models, and have been widely used in researches of different fields. The era of digital economy brings massive data to the economic field, but also poses many challenges to economic research. The machine learning method can fully mine the nonlinear and non-stationary information in the data and effectively improve the accuracy of economic analysis results. The integration of machine learning and economic problems has changed the research paradigm of traditional economics. This paper reviews the application of machine learning methods in economic research in recent years, then summarizes from the perspectives of inflation, exchange rate and currency, GDP, labor market, social stability, policy evaluation, etc., and finally compares the advantages and disadvantages of commonly used machine learning methods. The paper also presents the evaluation criteria of the model, such as root mean square error, F1-score, AUC value, etc.
作者
蒋锋
张文雅
Jiang Feng;Zhang Wenya(School of Statistics and Mathematics,Zhongnan University of Economics and Law,Wuhan 430073,China)
出处
《统计与决策》
CSSCI
北大核心
2022年第4期43-49,共7页
Statistics & Decision
基金
国家自然科学基金资助项目(61773401)
湖北省社会科学基金资助项目(2020209)。
关键词
机器学习
神经网络
支持向量机
朴素贝叶斯
集成学习
machine learning
neural network
support vector machine
naive Bayes
ensemble learning
