Bayesian Econometrics

Subject 316-407 (2009)

Note: This is an archived Handbook entry from 2009. Search for this in the current handbook

Credit Points: 12.50
Level: 4 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2009:

Semester 2, - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: Two 1.5-hour lectures per week (Semester 2)
Total Time Commitment: Not available

316-470 Econometric Techniques or equivalent.

Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support:


Dr Liana Jacobi
Subject Overview:

Basic tools and characteristics of Bayesian inference and the application of Bayesian inference to a number of econometric models are considered. The tools and characteristics will include joint, conditional and marginal probability distributions, prior, posterior and predictive distributions, Bayes theorem, representing uncertain information, and the estimation of moments and other integrals via Markov chain Monte Carlo techniques. The econometric models will include the traditional regression model, the seemingly unrelated regressions model, probit and tobit models and some time-series models.

Objectives: .

A 2-hour end-of-semester examination (60%) and class assignments up to 4000 words in total (40%).

Prescribed Texts: None
Recommended Texts:

Information Not Available

Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:
  • High level of development: evaluation of data and other information; synthesis of data and other information; critical thinking; interpretation and analysis; use of computer software; statistical reasoning; problem solving; collaborative learning; written communication; oral communication.

  • Moderate level of development: receptiveness to alternative ideas; application of theory to practice.

  • Some level of development: accessing data and other information from a range of sources.


Students may not gain credit for both 316-407 Bayesian Econometrics and 316-672 Bayesian Econometrics.

Related Course(s): Postgraduate Diploma In Economics

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