- Code:
- ECOM-410/DPE-9410
- Field:
- Macroeconomics
- Targets:
- Master’s students Research Master's students PhD students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Timm Prein
- Period:
- Period 1
- Format:
- Lecture
- Method:
- Contact teaching
- Venue:
- Economicum
- Enrollment:
In case of conflicting information consider the Sisu/Course/Moodle pages the primary source of information.
Aalto, Hanken and UH economics students can enroll through their home university’s SISU. Further instructions are available on the How to enroll? page, also for students from other universities.
If you would like to count the credits towards your degree, please check your curriculum or contact your supervisor or student services for guidance.
- To access the Moodle course area, use all the features and participate in the activities (assignments, discussions), you must have successfully registered for the course in Sisu and logged in with your UH user ID.
- For more information on how to activate your UH user ID and register for a Moodle course area, click here.
Course schedule in Sisu and Course page will be published in July
Content
The goal of the course is to provide an introduction to the methods of modern applied, quantitative macroeconomics. The course builds on existing dynamic stochastic (general) equilibrium models. The aim is to learn to solve numerically macroeconomic models for which closed-form solutions are not available. We will focus on both the basics of global solution algorithms and solution algorithms of linear rational expectation models. We will calibrate the model parameters and evaluate the quantitative performance of the models compared with empirical observations.
Students apply the learned methods, i.e. implementing and solving macroeconomic models, in a series of coding exercises. At the end of the course, the students submit a term paper and codes on a final project where they attempt a (partial) replication of a paper of their own choice from the literature. The project may include a calibration exercise, a policy experiment, or a computational exercise.
Learning outcomes
After the course, the student should
- Understand the solution algorithms of linear rational expectation models
- Understand the basics of global solution algorithms
- Be able to understand common equilibrium concepts
- Be able to code the model with a matrix programming language
- Be able to calibrate the model