Bayesian Econometrics

2019-2020

Course Objective

This course in the minor Applied Econometrics is targeted at Bachelor
Econometrics students and Bachelor students with different backgrounds
who have already had an introduction to programming and
econometrics/statistics. The objective is to acquaint the student with
Bayesian statistics and applications thereof to econometric problems,
using advanced computational methods.

Course Content

This course (named Computational Econometrics (E_EOR3_CE) in the past
academic years) will cover Bayesian statistics where the topics include
the prior and posterior density, Bayesian hypothesis testing, Bayesian
prediction, and Bayesian Model Averaging for forecast combination.
Several models will be considered, including the Bernoulli/binomial
distribution, the Poisson distribution and the normal distribution.
Obviously, attention will be paid to the Bayesian analysis of linear
regression models. Also simple time series models will be considered. An
important part of the courses is the treatment of simulation-based
methods such as Markov chain Monte Carlo (Gibbs sampling, data
augmentation, Metropolis-Hastings method) and Importance Sampling, that
are often needed to compute Bayesian estimates and predictions and to
perform Bayesian tests.

Teaching Methods

Lectures and exercises in the computer lab.

Method of Assessment

Final written exam – Individual assessment.
Exercises - groups of 1 or 2 students.

Literature

Slides and exercises that will all appear on Canvas.

Recommended background knowledge

Programming, Econometrics I, Numerical Methods.

General Information

Course Code E_EOR3_BE
Credits 6 EC
Period
Course Level 300
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. L.F. Hoogerheide
Examiner dr. L.F. Hoogerheide
Teaching Staff

Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: