DRE XX24 Topics in Macroeconomics I

DRE XX24 Topics in Macroeconomics I

Course code: 
DRE XX24
Department: 
Economics
Credits: 
3
Program of study: 
Doktorgradsstudium
Course coordinator: 
Hilde Christiane Bjørnland
Fabio Canova
Product category: 
PhD
Portfolio: 
PhD Economics courses
Semester: 
2021 Autumn
Active status: 
Planned
Teaching language: 
English
Course type: 
One semester
Introduction

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

Learning outcomes - Knowledge

The students should master and produce sophisticated research on dynamic general equilibrium models and models of international macroeconomics.

Learning outcomes - Skills

After taking this course the students should have a solid knowledge of numerical methods used to solve problems in macroeconomics and understand basic and advanced models of open economy and monetary policy.

General Competence

The students should be able to build and estimate models so as to analyse research questions that are at the frontier in macroeconomics. 

Course content

This course covers lectures 1- 6
Lecture 1: Calibration of DSGE models
Lectures 2-3: Maximum likelihood estimation of DSGE models
Lectures 4-5: Bayesian analysis and Markov chain Monte Carlo methods
Lecture 6: Bayesian estimation of DSGE models

Lecture 7-12 is covered in DRE XX25 Topics in Macroeconomics II
Lecture 7: Identification issues, DSGE-VARs, Choice of data for estimation.
Lecture 8: Prior and measurement errors specification problems
Lecture 9: Semi-structural DSGE; Data rich estimation
Lecture 10: Trends and non-balance growth paths; Eliciting priors from the data
Lecture 11: Prior Predictive analysis; Time varying coefficients DSGEs.
Lecture 12: Misspecification: composite likelikood and quasi-Bayesian methods.

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.

Software tools
Matlab
Additional information

-

Qualifications

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 course leader. 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.

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 categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
DRE XXXX1
Grading scale:
Pass/fail
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
100No1 Month(s)Individual Written assignment ( individually ) consisting of a maximum of 10 pages (plus references and appendix ).
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration:1 Month(s)
Comment:Written assignment ( individually ) consisting of a maximum of 10 pages (plus references and appendix ).
Exam code:DRE XXXX1
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Group work / Assignments
30 Hour(s)
Specified learning activities (including reading).
Student's own work with learning resources
40 Hour(s)
Teaching on Campus
15 Hour(s)
Sum workload: 
85

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.