DRE 7040 Topics in Macroeconomics I
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.
The students should master and produce sophisticated research on dynamic general equilibrium models closed and open economy macroeconomics.
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.
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.
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
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.
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.
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||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination when next scheduled course
|100||No||1 Month(s)||Individual||Written assignment ( individually ) consisting of a maximum of 10 pages (plus references and appendix ).|
|Form of assessment:||Written submission|
|Comment:||Written assignment ( individually ) consisting of a maximum of 10 pages (plus references and appendix ).|
|Exam code:||DRE 70401|
|Resit:||Examination when next scheduled course|
Group work / Assignments
Student's own work with learning resources
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.