DRE 7046 Inference in Macro Models: Forcasting with Big Data, BVARs and SVARs

DRE 7046 Inference in Macro Models: Forcasting with Big Data, BVARs and SVARs

Course code: 
DRE 7046
Department: 
Economics
Credits: 
4
Course coordinator: 
Hilde Christiane Bjørnland
Course name in Norwegian: 
Inference in Macro Models: Forcasting with Big Data, BVARs and SVARs
Product category: 
PhD
Portfolio: 
PhD Economics courses
Semester: 
2022 Spring
Active status: 
To be discontinued
Level of study: 
PhD
Discontinued term: 
2022 Autumn
Teaching language: 
English
Course type: 
One semester
Introduction

Norges Bank and BI Norwegian Business School are organizing a joint PhD course in Inference in Macro Models: Forcasting with Big Data, BVARs and SVARs. The course is registered with Department of Economics at BI. Guest lecturer will be: Professor Domenico Giannone, University of Washington and CEPR, and Professor Giorgio Primiceri, Northwestern University. The course will be hosted by Norges Bank over the period May 9-13, 2022. 

Learning outcomes - Knowledge

After taking this course the students should have a solid knowledge on inference in macroeconomic model and on how to produce forecasts and conduct semi-structural analysis with BVARs and SVARs. The students should master and be able to produce research on issues related with empirical macroeconomic models.

Learning outcomes - Skills

Upon completion of the course, the student should be able to specify, estimate and forecast with applied macroeconomic models such as BVARs and SVARs, based on modern software and be able to analyse and interpret the results. A central learning outcome is to be able to communicate the results in a scientific manner.

General Competence

The use of time series methods to address macroeconomic issues implies general knowledge on model specification, estimation, identification, forecast and analysis. After completing the course, the students should be able to discuss both possibilities and limitation in using the set of tools, and be able to reflect upon the final results and the policy implication of those.

Course content

1. Big data and the curse of dimensionality in macroeconomics
2. Introduction to Bayesian inference
3. Multivariate models: VARs and Bayesian VARs

4. Recent and more advanced priors

5. Shock identification and Structural VARs

Teaching and learning activities

The course will we taught in 5 intensive modules for a total of 20 hours. Students are required to participate in class.

Software tools
Matlab
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

Students are expected to be familiar with the basics of time series analysis. Admission to a PhD Program is a general requirement for participation to a PhD course. External candidates are kindly asked to attach confirmation of admission to a PhD program when signing up for a course with the doctoral administration. Candidates can be allowed to sit in on courses by approval of the course leader. Sitting in on courses does not permit registration for courses, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.

Assessments
ExamWeightInvigilationDurationSupport materialsGroupingComment
Exam category
Submission
Form of assessment
Written submission
Exam code
DRE 70461
Grading scale
Pass/fail
Grading rule
Internal examiner
Resit
Examination when next scheduled course
100
No
1 Month(s)
Individual
An individual assignment consistent of maximum 10 pages (plus references and appendix). The final grade is pass/fail.
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Student's own work with learning resources
66 Hour(s)
Examination
20 Hour(s)
Teaching
24 Hour(s)
20 hours teaching and 4 hours seminar and Q&A
Sum workload: 
110

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 4 ECTS credit corresponds to a workload of at least 110 hours.