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- 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 and Hanken economics students can enroll in their home university’s SISU! Further instructions can be found on the How to enroll page, also for other students.
Before taking and completing the course make sure that the credits can be counted towards your degree at your home university by checking which courses are included in your curriculum or by contacting your home university’s student/learning services.
Please note that there is a different code for UH PhD students: DPE-9410
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