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
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.
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.
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.
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.
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
The course will we taught in 5 intensive modules for a total of 20 hours. Students are required to participate in class.
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.
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.
| Exam category | Weight | Invigilation | Duration | Grouping | Comment exam |
|---|---|---|---|---|---|
| Exam category: Submission Form of assessment: Written submission Exam code: DRE 70461 Grading scale: Pass/fail Grading rules: 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. |
| Activity | Duration | Comment |
|---|---|---|
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 |
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.