AØKK08333U Seminar: Bayesian Econometrics - cancelled
The goal of this seminar is to allow students to put into practice their knowledge and to gain practical experience with Bayesian econometric methods. Students will be able to choose the topic they want to explore, which can be either empirical or theoretical.
Bayesian methods offer a fresh perspective to econometrics, as they allow to tackle complicated estimation problems in a tractable way. These approaches usually rely on simulation methods and can therefore have an advantage over classical methods. For instance, unobserved variables (like latent utilities or random effects) can be difficult to integrate out of a likelihood function, but are generally straightforward to simulate in a Bayesian framework.
At the end of the seminar, students will:
Have reviewed the relevant literature related to the topic they have chosen, and understand the state of the art as well as the limitations of the current approaches.
Have a grasp of simulation methods, understand their principle and how they can be used to make inference.
Demonstrate an ability to select the most appropriate method for the topic they have chosen.
Be able to implement Markov chain Monte Carlo methods, both theoretically (analytical derivation of the algorithm) and practically (programming).
Demonstrate technical skills in writing code to implement Bayesian methods.
Students will be able to conduct a full Bayesian analysis: (1) formulate an economic model, (2) organize prior knowledge and ”beliefs” about the model (prior), (3) use relevant data to express the observed information in the model (likelihood), (4) use Bayes' theorem to update beliefs (posterior), (5) derive an appropriate algorithm to compute the posterior distribution, (6) write code to implement the algorithm, (7) interpret the results and criticize the model.
Lynch, Scott M. (2007). Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. Springer. ISBN 978-0-387-71264-2.
Lancaster, Tony (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing. ISBN 978-1-405-11720-3.
Other references and scientific articles will be suggested to the students based on the subject they decide to study.
Students should have a sound knowledge of basic elements of probability (marginal, conditional and joint distribution of random variables, law of large numbers, central limit theorem, likelihood principle, etc.) and of standard econometric methods (maximum likelihood estimation, method of moments, etc.).
Students should have good programming skills. The R language will be used in this seminar, as it provides many freely available packages implementing Bayesian methods. It is, however, not a prerequisite, and students will be allowed to use a different language (e.g., Matlab).
Before the session a "so-finalized-as-possible"-draft of the paper must be uploaded in Absalon. After the presentations, the student submit an edited version of the paper in the Digital Exam portal as the final exam paper. The aim is that students use the presentation sessions as an opportunity to receive and use the constructive feedback to improve the paper.
• Planning meeting: September 4th 2017, 13-15
• Deadline commitmentpaper: September 30th
• Deadline of pre-paper uploadet to Absalon: One week before presentations
• Presentations/Workshops: Week 47 (i.e Nov 20-21, dates will be determined at the first class meeting).
Individual supervision meetings will be organized with each group during the semester to discuss the progress of the work and potential problems.
Read about the study programme and curricula at MSc in Economics
- 7,5 ECTS
- Type of assessment
- Written assignment- a seminar paper in English that meets the formal requirements for written papers stated in the curriculum and at KUNet for seminars.
- Exam registration requirements
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Exam period
Deadline for uploading the seminar paper to DE: 1st of December 2017 before 10:00 AM
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course and the objectives stated in the Curriculum.
- Project work