Zsófia Bárány
Associate Professor
Miklós Koren
Professor
The course introduces two building blocks of macroeconomic modeling: forward-looking dynamic models and general equilibrium with heterogeneous agents. These tools are applied to problems of economic growth, labor market search, and industry dynamics. Quantitative model solutions are also illustrated using the Julia programming language.
The course introduces two building blocks of macroeconomic modeling: forward-looking dynamic models and general equilibrium with heterogeneous agents. These tools are applied to problems of economic growth, labor market search, and industry dynamics. Quantitative model solutions are also illustrated using the Julia programming language.
Dynamics and equilibrium are two building blocks of macroeconomic thinking. These approaches are essential for studying a wide range of problems in macroeconomics, labor economics, industrial organization, economic geography, international trade.
By the end of the course, students will be able to
Learning objectives will be achieved through
Grading will be based on the total score out of 100, in line with CEU’s standard grading guidelines.
| Session | Topics |
|---|---|
| Weeks 1-2 | Growth facts, the Solow and the Ramsey model |
| Weeks 3-5 | Overlapping generations model and introduction to dynamic programming |
| Weeks 6-10 | Linear algebra, dynamic programming and ODEs in Julia |
| Weeks 11-12 | Endogenous growth |
| Weeks 13-16 | Search and matching models of the labor market |
| Weeks 17-18 | Industry equilibrium models of heterogeneous firms |
| Weeks 19-20 | The Hopenhayn model of industry dynamics |
| Weeks 21-22 | Solving industry dynamics models in Julia |
| Weeks 23-24 | Input-output linkages |
This course teaches how to organize data and code on your computer, how to write simple programs in Python to automate tasks, and how to use Stata throughout the steps of the your research process.
Data engineering is increasingly important to leverage the value created by data scientists and analysts. Executives who understand the basics of data engineering can help their team create data products that are easy to change in response to ever changing business requirements. This course offers a high-level overview of the types of decisions data engineers have to make, and a hands-on illustration of the most common problems on real-world data. The key goal of this course is to help executives make decisions about the data analytics efforts of their business and ask the right questions from their team. This will help increase the Return On Investment of analytics projects so that data can serve as a competitive advantage of the business.