- Code:
- ECOM-412/DPE-9412
- Field:
- Econometrics
- Targets:
- Master’s students Research Master's students PhD students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Jani Luoto
- 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.
Content
The course provides an introduction to the empirical implementation of DSGE models and deepens the knowledge of structural VAR models. In addition, the relationship between these two main approaches to empirical research in macroeconomics are discussed, including the validation of DSGE models by vector autoregressions. Finally, identification of the structural VAR model by sign restrictions, often derived from DSGE models, is covered. The emphasis is on the practical application of the methods discussed in modelling macroeconomic data.
Learning outcomes
After the course, the student should
- Be familiar with the solution methods of dynamic stochastic general equilibrium (DSGE) models to the extent needed for their empirical implementation
- Be able to estimate DSGE models, and use them for forecasting and dynamic analysis
- Be aware of the relationship between vector autoregressive (VAR) and DSGE models
- Understand how DSGE models can be validated by means of vector autoregressions, and be familiar with DSGE-VAR models
- Know how structural VAR models can be identified by sign restrictions
- Be able to apply methods of classical and Bayesian statistical inference in DSGE and structural VAR models
- Be able to report empirical research results obtained using the methods covered