Bayesian Econometrics

Subject ECOM40002 (2011)

Note: This is an archived Handbook entry from 2011.

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

This subject has the following teaching availabilities in 2011:

Semester 2, Parkville - 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
Prerequisites:

The following:

Subject
Study Period Commencement:
Credit Points:
Corequisites: None
Recommended Background Knowledge: Please refer to Prerequisites and Corequisites.
Non Allowed Subjects: Students may not gain credit for both ECOM40002 Bayesian Econometrics and ECOM90010 Bayesian Econometrics.
Core Participation Requirements: For the purposes of considering requests for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements for this entry.

The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/

Coordinator

Dr Liana Jacobi

Contact

ljacobi@unimelb.edu.au

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: Information not available.
Assessment:

A 2-hour end-of-semester examination (60%) and class assignments up to 4000 words in total (40%).
Prescribed Texts: You will be advised of prescribed texts by your lecturer.
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.
Notes:

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

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