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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
VOR-ORT
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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)

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

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

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 26.09.2022 11:28