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- Code:
- ECOM-411/SOC-9411
- Field:
- Econometrics
- Targets:
- Master’s students Research Master's students PhD students
- Organiser:
- University of Helsinki - Economics
- Instructor:
- Henri Nyberg
- Period:
- Period 4
- Format:
- Participation in teaching
- Method:
- Distance teaching
- Enrollment:
Equivalent to ECOM-411 Applied Macroeconometrics 1
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 methods of modern applied macroeconometrics. The main emphasis is on the most widely employed framework in applied research and policy work, the structural vector autoregressive (SVAR) model, but other approaches are also briefly reviewed, and their relation to the SVAR model is discussed.
The course starts with a brief review of the reduced-form vector autoregressive (VAR) model and proceeds to the identification of economic shocks by various methods and the use of the SVAR framework in empirical research and policy analysis. Short- and long-run identification restrictions as well as identification based on instrumental variables and statistical properties of the data are considered. Classical statistical inference in reduced-form and structural VAR models as well as tools of structural analysis, including the impulse response function, forecast error variance decomposition, historical decomposition and counterfactual analysis, are covered. The structural VAR approach is contrasted with alternatives, including DSGE and dynamic simultaneous equations models as well as local projection regressions.
The emphasis is on practical application of the methods in structural modelling of macroeconomic and other data.
Learning outcomes
After the course, the student should
- is familiar with the main approaches to structural modelling macroeconomic and other time series data,
- knows the basic properties of the linear vector autoregressive (VAR) model,
- understands the concept of identification of economic shocks in structural VAR models, and is able to conduct structural analysis in the VAR model identified by short-run and long-run identification restrictions as well as instrumental variables and structural properties of the data,
- is able to apply methods of classical statistical inference in reduced-form and structural VAR models,
- is able to report empirical research results obtained using the methods covered.