This module will introduce students to three topics in computational economics.
First, we will develop an agent-based computational economics model (ACE). Lectures are accompanied by laboratory sessions which will cover macro-economic modelling, software development, object-oriented programming and implementation. Students will learn how to design, programme and implement ACE models with specific applications to building large-scale data-driven flow of funds models, and financial network type agent-based macro-economic models. The fundamental of simulation techniques is also covered.
Second, students will learn a structural model in labour economics. We will develop the main tools of the search-matching approach. Several policy issues can be addressed using these models, e.g., the design of unemployment insurance systems, the impact of hiring and firing costs, etc. We discuss both theoretical and empirical literature.
In the final part of the course, students will learn the basics of data-analysis using large-scale data sets. The basics of "data science" developed here will focus on data visualization and some elementary applications of machine learning.
Classroom lectures will be accompanied by lab classes. All the computation will be done in R and/or Python.