Applied Macroeconometrics 2 (5 cr)

ECOM-412 (MSc) / DPE-9412 (PhD)
Master’s students Research Master's students PhD students
University of Helsinki - Economics
Jani Luoto
Period 1
Contact teaching
Economicum building

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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.

  • Schedule: can be found in Course Page and Sisu
  • Study materials: can be found in Moodle
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    • More tips for enrolling in Moodle can be found here

Please register for the course in the UH Sisu with your UH username. Further instructions can be found here.

Aalto University Students
  • Code: no equivalent code
  • Target groups: MSc / rMSc / PhD
  • Credit points: 5
  • Credit transfer: apply for inclusion in Sisu
Hanken Students
  • Code: 26028
  • Target groups: MSc / rMSc / PhD
  • Credit points: 5
  • Credit transfer: apply for substitution in Sisu
University of Helsinki Students
  • Code: ECOM-412 (rMSc) / DPE-9412 (PhD)
  • Target groups: MSc / rMSc / PhD
  • Credit points: 5
  • AGERE students: Before taking and completing this course make sure that the credits can be counted towards your degree by checking which courses are/can be included in your curriculum. You can also contact your planning officer Simo Riikonen (
FDPE Students Students
  • 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

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