AI for Economics Analysis

  • Credits: 3
  • Ending: Examination
  • Range: 2C
  • Semester: summer
  • Year: 2
  • Faculty of Economics and Finance

Teachers

Included in study programs

bAE_21

Teaching results

Teaching results:
The course introduces students to how Artificial Intelligence (AI) can be used in economic analysis, it focuses on practical applications, technical details are discussed only in required minimum. Students will explore how AI can help in economic forecasting, market analysis, consumer behavior studies, or policy-making. In the course, also real-world examples will be included, and simple hands-on exercises with AI-driven tools used in economics will be used to ensure the development of AI skills of students.
Learning Outcomes
By the end of the course, students will have developed the following competencies and skills:
1. Knowledge and Understanding
• Understand the basic principles of AI and its relevance to economic analysis.
• Identify key AI applications useful for economic analysis, market analysis, and policy-making.
• Recognize the opportunities and risks of the use of AI in analyzing financial markets, business strategies, employment trends.
2. Analytical and Critical Thinking Skills
• Assess the impact of AI on decision-making in economics, business, and policy.
• Analyze real-world case studies of AI applications in economics.
• Evaluate the limitations and ethical considerations of AI in economic contexts.
3. Practical and Applied Skills
• Use basic AI-powered economic tools (e.g., Google Trends, economic dashboards) for economic analysis.
• Evaluate quality of AI-generated economic data.
• Communicate AI-driven economic insights and trends through reports and presentations.
4. Digital and Data Literacy
• Develop a basic understanding of big data and how AI processes economic information.
• Identify and assess reliable AI-driven economic data sources (e.g., OECD, IMF, World Bank).
• Identify AI-based decision-support systems used in economic research and business analytics.
5. Communication and Collaboration
• Engage in critical discussions about AI's role in economics.
• Work in teams on economic problems.
• Communicate AI-driven economic insights in a structured manner to a non-technical audience.

Indicative content

Indicative content:
1. Introduction to AI in economics – What AI is and how it is changing economics.
2. How AI analyzes economic data – AI’s role in economic research, simple AI tools for data analysis.
3. AI in economic forecasting – How AI predicts inflation, GDP, and employment trends.
4. AI in market research and consumer behavior – How businesses use AI to understand consumers.
5. Big data in economics – How large datasets improve economic analysis.
6. AI in finance and banking – AI in stock markets, fraud detection, and credit scoring.
7. AI and public policy – How AI helps governments make better economic decisions.
8. AI and jobs: Risks and opportunities – The impact of AI on the labor market.
9. Ethical concerns in AI and economics – AI bias, privacy, and fairness in economic decisions.
10. AI and business strategy – How AI helps companies make smarter economic decisions.
11. Limitations of AI in economics – What AI can and cannot do in economic analysis.
12. Case studies and real-world examples – How companies and governments use AI today.
13. Future of AI in economics – Discussion on trends and final presentations.

Support literature

Literature:
1. Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic policy for artificial intelligence. Innovation policy and the economy, 19(1), 139-159.
2. Korinek, A. (2023). Generative AI for Economic Research: Use Cases and Implications for Economists. Journal of Economic Literature 61 (4): 1281–1317.
3. Aldasoro, I., Gambacorta, L., Korinek, A., Shreeti, V., Stein, M. (2024). Intelligent financial system: how AI is transforming finance. BIS Working Papers, No. 1194
Additional resources will be provided in class.

Requirements to complete the course

60 % - seminar work
40 % - written exam

Student workload

Total study load (in hours): 78 hours
26 hours – direct instruction
20 hours - preparation of a team case study
32 hours - preparation for the final exam

Date of approval: 12.03.2025

Date of the latest change: 12.03.2025