University of Technology Sydney

23992 Bayesian Econometrics for Research

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Business: Economics
Credit points: 0 cp

Subject level:

Postgraduate

Result type: Pass fail, no marks

There are course requisites for this subject. See access conditions.
Anti-requisite(s): 23972 Bayesian Econometrics for Research

Description

This subject provides Economics PhD students with training at the frontier of research in Bayesian econometrics. It emphasises the recent development in theoretical Bayesian econometrics as well as their applications.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:
1. Formalise economic theory into Bayesian statistical models and interpret estimates
2. Conduct their own Bayesian econometric analysis using recently developed methods
3. Evaluate state-of-the-art theoretical and applied Bayesian econometrics research

Contribution to the development of graduate attributes

This subject enables students to develop a comprehensive knowledge in a field of study. More specifically, it contributes to the development of the following graduate attributes:

  • Business knowledge and concepts
  • Critical thinking and analytical skills
  • Business practice oriented skills

Teaching and learning strategies

The subject will be taught using a combination of lecture and discussion. Students will read from texts and articles appropriate to the selection of topics.

Content (topics)

Content is to be determined by each coordinator. It will typically cover (but not limited to):

  1. Monte Carlo methods
  2. Hierarchical models
  3. Dynamic Models
  4. Sequential Monte Carlo methods

Minimum requirements

Students must complete to a satisfaction level the requirements of the agreed learning contract.

Required texts

There is no required textbook for this course. Lecture slides will be uploaded on UTSOnline before the class meets.

References

References will be provided throughout the semester.