Universität Wien
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040162 KU Introduction to Bayesian econometrics (MA) (2022W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
ON-SITE
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 04.10. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 11.10. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 18.10. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 25.10. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 08.11. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 15.11. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 22.11. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 29.11. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 06.12. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 13.12. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 10.01. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 17.01. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 24.01. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 31.01. 18:30 - 20:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

This class is aimed at introducing students to Bayesian econometrics and inference. Theoretical inputs are complemented with coding sessions (with the software R), where the underlying concepts are applied to artificial and real datasets. This serves to give students the necessary tools to develop their own Bayesian econometric methods, introduces them to statistical computation and provides further intuition on Bayesian concepts.

The lecture covers the following topics:
1. Probabilities and random variables
2. Fundamentals of Bayesian inference
3. Simulation-based methods and statistical programming
4. Bayesian linear regression and model selection
5. Time series and predictive inference
6. Extensions (optional depending on our progress)

Assessment and permitted materials

Grades will be determined via three aspects in the following proportions:
– Homework (total 50%): There will be two homework assignments (25% each) during the semester. These can either be solved individually or in groups of two.
– Final exam (50%): The final exam will be a take-home exam, to be solved individually.

The homework assignments and the final exam are open book, that is, students may use any materials or software if it is properly referenced. Both must be handed in as a single PDF file.

Minimum requirements and assessment criteria

There are no preliminary requirements for taking this class. However, it would be beneficial to have prior knowledge of basic probability and statistics alongside classical econometrics. To achieve a positive grade, students will have to achieve both 50% of the maximum homework grade and 50% of the maximum final grade.

Examination topics

1. Fundamentals of Bayesian inference
2. Simulation-based methods and statistical programming
3. Bayesian linear regression and model selection
4. Time series and predictive inference

Reading list

Materials for the lecture will in part be provided in the form of slides and computer code. These materials are based on:
Chan, J., Koop, G., Poirier, D.J. and J.L. Tobias: “Bayesian Econometric Methods,” Cambridge University Press.

Association in the course directory

Last modified: Mo 26.09.2022 11:28