This module will introduce students to three topics in computational economics.
First, we will develop an agent-based computational economics model (ACE). Students will learn how to design, program, and implement an ACE model with financial networks.
Second, we will cover essential tools of modern labour economics, with a focus on search and matching models and their application to macroeconomic modelling. Students will learn how these models can be used to address policy issues such as unemployment and wage inequality. The fundamental of estimation techniques will also be covered.
In the final part of the course, students will learn data analysis using large-scale data sets. The "data science" developed here will focus on machine learning (ML) and how this technique can be applied to real-world economic and financial problems.
Classroom lectures will be accompanied by lab classes. All the computation will be done in R or Python.
- Module Supervisor: Ran Gu