Course ObjectiveThis 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 ContentThis 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 MethodsLectures and exercises in the computer lab.
Method of AssessmentFinal written exam – Individual assessment.
Exercises - groups of 1 or 2 students.
LiteratureSlides and exercises that will all appear on Canvas.
Recommended background knowledgeProgramming, Econometrics I, Numerical Methods.
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||dr. L.F. Hoogerheide|
|Examiner||dr. L.F. Hoogerheide|
You need to register for this course yourself
|Teaching Methods||Lecture, Study Group|
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