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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 and the DSGE-VAR model. 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.

  • Completion method: contact teaching
  • Schedule: can be found in Course Page and Sisu
  • Study materials: can be found in Moodle
    • A link and a Moodle course key will be sent by email before the course starts and/or they will be provided on the Courses page: you can view the information on this site without logging in or registering, but some of the content added by teachers to course pages may be available to course participants only, for example Moodle course enrolment key.
    • Log in with your UH username to be able to use all the features of the course workspace

Please register for the course in the UH Sisu with your UH username. Further instructions (link to be added here later).

    • Code: no equivalent code

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Credit transfer: apply for inclusion in Sisu

    Further instructions (link to be added here later)

    • Code: 26028

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Credit transfer: apply for substitution in Sisu

    Further instructions (link to be added here later)

    • Code: ECOM-412 (rMSc) / DPE-9412 (PhD)

    • Target groups: MSc / rMSc / PhD

    • Credit points: 5

    • Please contact your supervisor/program director to be sure that the course credit can be counted towards your degree

    • Credit transfer: please apply for credit transfer according to your home university’s procedures

    Further instructions (link to be added here later).Further instructions (link to be added here later).

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