DRE 7040 Topics in Macroeconomics I

DRE 7040 Topics in Macroeconomics I

Course code: 
DRE 7040
Course coordinator: 
Fabio Canova
Course name in Norwegian: 
Topics in Macroeconomics I
Product category: 
PhD Economics courses
2021 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course presents tools needed for estimation, evaluation, and inference with dynamic stochastic general equilibrium macroeconometric models in academic and policy institutions. Recent work on matching VARs and DSGE dynamics, and on measuring gaps/potentials/star variables and their relationship with model based quantities are discussed.

Learning outcomes - Knowledge

The students should master and produce sophisticated research on dynamic general equilibrium models  closed and  open  economy macroeconomics.

Learning outcomes - Skills

After taking this course the students should have a solid knowledge of numerical methods used to solve macroeconomic models, of  procedures  to  numerically  simulate  the  posterior  distribution of structural  parameters, and evaluate the ability of  monetary models  to fit  the  data.

General Competence

The students should be able to build and estimate dynamic  structural  models,  test  their  validity  against  the  data  and  alternative  models and  produce  conditional and  unconditional  forecasts. In  addition,  they  should  be  able formally compare  outcomes with  those  of  time  series  models, such as  BVARs.

Course content

Lecture 1: Calibration and  evaluation of DSGE models
Lectures 2: Maximum likelihood estimation of DSGE models
Lectures 3-4: Bayesian analysis and Markov chain Monte Carlo methods
Lecture 5: Bayesian estimation of DSGE models. Evaluation and  forecasting.
Lecture 6: Practical and  policy  oriented DSGE models. Composite Likelihood

Teaching and learning activities

Students are required to participate in class – both in discussions and by presenting models/material from the reading lists  as well as solve and hand-in solutions to exercises and problems.

Familiarity with Matlab is a prerequisite. Knowledge of Dynare and Rise a plus.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information



Enrollment in a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.


Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

To properly follow the course, participants must be familiar with DSGE models, and must have followed the basic first year time series course. Familiarity with Matlab is a prerequisite. Knowledge of Dynare and Rise a plus.

Exam category: 
Form of assessment: 
Written submission
1 Month(s)
Written assignment ( individually ) consisting of a maximum of 10 pages (plus references and appendix ).
Exam code: 
DRE 70401
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Group work / Assignments
30 Hour(s)
Specified learning activities (including reading).
Student's own work with learning resources
40 Hour(s)
15 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 3 ECTS credit corresponds to a workload of at least 80 hours.