Computational Economics

This course aims to equip master's/high undergraduate’s-level students in economics with the computational tools, programming skills, and modeling techniques required to tackle complex economic problems. By integrating modern computational methods with the powerful capabilities of Python, this module bridges theoretical economics with practical, data-driven solutions. It prepares students to apply these skills in academic research, policy-making, and industry. The objectives include:

-Develop Computational Proficiency in Python:
Equip students with hands-on programming skills in Python, emphasizing its application to economic modeling, data analysis, and visualization.
-Understand Core Numerical Methods:
Introduce numerical techniques for solving optimization problems, simulating dynamic systems, and computing equilibrium models in economic contexts.
-Build and Analyse Economic Models:
Enable students to design and implement computational models, including dynamic programming, general equilibrium analysis, and agent-based simulations, using Python.
-Leverage Machine Learning for Economics:
Teach students how to apply Python-based machine learning tools for economics problems.