DRE 7030 Introduction to Bayesian Econometrics
This is a course in Bayesian econometrics at the PhD level. This includes:
1. A knowledge of Bayesian theory
2. Posterior simulation: Monte Carlo Integration, Gibbs Sampling and Metropolis-Hastings
3. Application of various models: linear regressions, vector autoregressions and state-space model
After completion of the course, the students should have foundations for applying Bayesian Econometrics methods. This includes being able to:
1. Understand both the theoretical and practical challenges underlying posterior simulation
2. Estimate various Bayesian models, including linear regression, vector autoregression and state space models
3. Be able to understand and create research articles with Bayesian econometric
After completing the course, the students should have developed the ability to contrast classical and Bayesian thinking in econometrics, and be able to apply Bayesian methods in qualitative research.
Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and seeing how Bayesian methods work in the familiar context of the regression model. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions (VARs) and state space models.
The course takes place over 4 days. Each day will consist of a two hour lecture in the morning and a two hour computer lab in the afternoon.
· Topic 1: An Overview of Bayesian Econometrics.
· Topic 2: Bayesian Inference in the Normal Linear Regression Model with Natural Conjugate Priors
· Topic 3: Bayesian Inference in the Normal Linear Regression Model with Independent Normal Gamma Prior
· Topic 4: Bayesian Methods for Regression Models with Big Data
· Topic 5: Bayesian Vector Autoregressions (VARs)
· Topic 6: Bayesian Inference in the Normal Linear State Space Model
· Topic 7: Stochastic Volatility
· Topic 8: Time-varying Parameter VARs
The exam will be a research paper in which course participant will be asked to produce an original piece of analysis using the tools learned in the course.
The course material will draw on the following readings. Koop (2003) and Chan, Koop, Poirier and Tobias (2019) are textbooks protected by copyright. The remaining readings are monographs or manuscripts which will be made available on the course website.
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.
|Exam category||Weight||Invigilation||Duration||Grouping||Comment exam|
Form of assessment:
Internal and external examiner
Examination when next scheduled course
|Form of assessment:||Written submission|
|Exam code:||DRE 70301|
|Resit:||Examination when next scheduled course|
Teaching on Campus
Feedback activities and counselling
Student's own work with learning resources
Prepare for teaching
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.