EC491 – Digital Economics & Applications of AI
Bachelor of Science in Economics
Core Course
EC491 – Digital Economics & Applications of AI
Course Unit Code: EC491
Type Of Unit: Elective
Level of Course Unit: First Cycle
Year of Study: Third/Fourth year
Semester: On demand
Number of ECTS Credits: 7.5
Mode of Delivery
Prerequisites
CS321- Machine Learning, Data Mining & Business Analytics
Course Objectives
The objective of the course is twofold. First, the course aims to provide a theoretical basis for digital economics and to show how these theories can be applied to the study of real-world economics and business phenomena. This course highlights the complex ecosystem and seeks to prepare students for success in the ever-evolving and complex realm of the Digital Economy. Second, the course aims to introduce students to state-of-the-art applications of machine learning (ML) in economics and forecasting. It explores how economists have utilized ML techniques to address emerging research inquiries, focusing on recent empirical economics studies employing ML models. The main objective of this course is to provide students with a comprehensive understanding of how AI techniques can be used to analyze economic data, make accurate predictions, and generate valuable insights for decision-making in various economic contexts. The main goal is that the students can understand and apply sophisticated models in economic applications.
Learning Outcomes
On completion of this course students are expected to:
CILO 1 Understand what is meant by digitization of the economy and how it is related to digitization of communication networks and production and storage of digital information;
CILO 2 Explain how information and communication technology has evolved toward increasingly complex systems and applications;
CILO 3 Understand digital and e-commerce markets
CILO 4 Understand the fundamental concepts of artificial intelligence (AI) and machine learning (ML) as applied to economic analysis;
CILO 5 Apply time-series forecasting methods to economic data;
CILO 6 Assess the performance of different predictive modeling techniques and select appropriate methods for specific forecasting scenarios;
CILO 7 Integrate machine learning techniques with traditional econometric forecasting models for improved predictive accuracy;
CILO 8 Develop specialized forecasting techniques for high-frequency trading and financial markets;
CILO 9 Understand the principles of agent-based modeling and simulation for studying complex economic systems;
CILO 10 Utilize NLP techniques to extract valuable information for economic forecasting from unstructured textual data sources;
CILO 11 Analyze real-world case studies and applications demonstrating the use of AI techniques in various economic domains;
Course Content
The Digital Economy CILO 1
Information and Communication Technologies CILO 2
Digital Market Modeling CILO 3
Big Data Economics CILO 4
Foundations of AI and Machine Learning in Economics and Forecasting CILO 4
Predictive Modeling CILO 5
Big Data and Econometrics CILO 6
Econometric Forecasting Models CILO 7
Forecasting in High-Frequency Trading CILO 8
Agent-Based Modeling CILO 9
Forecasting with Unstructured Data CILO 10
Case Studies and Applications CILO 11
Course Features
Planned learning activities and teaching methods Lectures; in-class discussion and debates; exercises (exercises, database, software etc.), group assignments
Assessment methods and criteria Class participation and problem sets: 20%
Group Project or Case Analysis: 30%
Final Exam: 50%
Language of Instruction English
Work Placement(s) Not applicable
Readings
Main Textbooks:
Øverby, Harald, and Jan Arild Audestad. Introduction to digital economics: Foundations, business models and case studies. Springer Nature, 2021.
Krohn, Jon, Grant Beyleveld, and Aglaé Bassens. Deep Learning Illustrated. Addison-Wesley Professional, 2019.
Optional textbook:
Nguyen, O. “Digital Economy and Its Components: A Brief Overview and
Recommendations”. (2023).
Goldfarb, A., & Tucker, C. “Digital economics. Journal of economic
literature”, 57(1), 3-43. (2019).
Artificial Intelligence: A Modern Approach (4th edition). Stuart Russell and
Peter Norvig, Pearson, 2020.
Rebooting AI: Building Artificial Intelligence We Can Trust. Gary Marcus and
Ernest Davis, Pantheon Books, 2019.
Articles & Journals:
Annor Antwi, Albert, and Ayman Abdulsalam Mohamed Al-Dherasi.
“Application of Artificial Intelligence in Forecasting: A Systematic
Review.” Available at SSRN 3483313 (2019).
Athey, Susan, and Guido W. Imbens. “Machine learning methods that
economists should know about.” Annual Review of Economics 11 (2019).
Aldridge, Irene. High-frequency trading: a practical guide to algorithmic
strategies and trading systems. Vol. 604. John Wiley & Sons, 2013.
Tang, Yuk-Ming, et al. “Forecasting economic recession through share price in the logistics industry with artificial intelligence (AI).” Computation 8.3 (2020): 70.
Drachal, Krzysztof, and Michał Pawłowski. “A review of the applications of genetic algorithms to forecasting prices of commodities.” Economies 9.1 (2021): 6.
English (UK) 
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