在大数据与人工智能时代,传统的计量经济学教学面临着新的挑战与机遇。本文通过分析数据类型的变化和经济学研究范式的转变,指出了传统计量经济学教学内容的局限性。针对这些局限性,提出了优化计量经济学教学内容的五项改革建议,包括引...在大数据与人工智能时代,传统的计量经济学教学面临着新的挑战与机遇。本文通过分析数据类型的变化和经济学研究范式的转变,指出了传统计量经济学教学内容的局限性。针对这些局限性,提出了优化计量经济学教学内容的五项改革建议,包括引入数据科学基础、融入机器学习方法、强化实践教学内容、平衡理论与应用以及推动模块化课程设计。这些改革旨在提升学生的数据处理能力、编程技能、批判性思维、跨学科应用能力以及团队合作能力。教学内容的优化不仅能够帮助学生适应现代经济研究的需求,还将为其未来的职业发展奠定坚实的基础。In the era of big data and artificial intelligence, traditional econometrics teaching faces new challenges and opportunities. In this paper, by analyzing the changes in data types and the shift in economic research paradigms, the limitations of traditional econometrics teaching content have been identified. To address these limitations, five reform suggestions for optimizing econometrics teaching content have been proposed: introducing data science fundamentals, incorporating machine learning methods, enhancing practical teaching content, balancing theory and application, and promoting modular course design. These reforms aim to improve students’ data processing capabilities, programming skills, critical thinking, interdisciplinary application abilities, and teamwork skills. The optimization of teaching content will not only help students adapt to the demands of modern economic research, but also lay a solid foundation for their future career development.展开更多
文摘在大数据与人工智能时代,传统的计量经济学教学面临着新的挑战与机遇。本文通过分析数据类型的变化和经济学研究范式的转变,指出了传统计量经济学教学内容的局限性。针对这些局限性,提出了优化计量经济学教学内容的五项改革建议,包括引入数据科学基础、融入机器学习方法、强化实践教学内容、平衡理论与应用以及推动模块化课程设计。这些改革旨在提升学生的数据处理能力、编程技能、批判性思维、跨学科应用能力以及团队合作能力。教学内容的优化不仅能够帮助学生适应现代经济研究的需求,还将为其未来的职业发展奠定坚实的基础。In the era of big data and artificial intelligence, traditional econometrics teaching faces new challenges and opportunities. In this paper, by analyzing the changes in data types and the shift in economic research paradigms, the limitations of traditional econometrics teaching content have been identified. To address these limitations, five reform suggestions for optimizing econometrics teaching content have been proposed: introducing data science fundamentals, incorporating machine learning methods, enhancing practical teaching content, balancing theory and application, and promoting modular course design. These reforms aim to improve students’ data processing capabilities, programming skills, critical thinking, interdisciplinary application abilities, and teamwork skills. The optimization of teaching content will not only help students adapt to the demands of modern economic research, but also lay a solid foundation for their future career development.
