DRE 7014 Bayesian Econometrics
Please note that this course will be revised before it is offered again.
Students should be able to read critically papers and to use Bayesian inference for their own research, in each case in relation to the material that has been covered.
1. Concepts for Bayesian Inference
- Bayesian inference
- Criteria for evaluating statistical procedures
- Probability: objective or subjective
2. Numerical Methods for Bayesian Inference
- Need for numerical integration
- Deterministic integration
- Monte Carlo integration
3. Bayesian Inference for Regression Analysis
- Regression with non-informative prior
- Regression with conjugate prior
- Partially linear model
- Regression with non-conjugate prior
- Heteroskedastic errors
- Autocorrelated errors
- IID student errors
4. Bayesian Inference for vector autoregressive models
- Unrestricted VAR and multivariate regression models
- Posterior with NIP
- Posterior with informative prior
- The Minnesota prior
- Restricted VAR and SURE models
5. Bayesian Inference for volatility models
- ARCH models
- Stochastic volatility models
A course of 3 ECTS credits corresponds to a workload of 80-90 hours.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on the course homepage/It's learning or text book.
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.
Knowledge of econometric models (regression models, qualitative and limited dependent variables, time series models). Ability at computer programming (e.g. in R, Matlab, GAUSS, Ox, C or any other language).
|Exam category||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
All components must, as a main rule, be retaken during next scheduled course
|Form of assessment:||Written submission|
|Resit:||All components must, as a main rule, be retaken during next scheduled course|
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.