Bayesian Econometrics (ECOM90010)
Graduate courseworkPoints: 12.5Not available in 2020
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
| Fees | Look up fees |
|---|
The overall aim of this subject is to introduce students to the essential concepts and techniques/tools used in Bayesian inference and to apply Bayesian inference
to a number of econometric models. Basic concepts and tools introduced include joint, conditional and marginal probability distributions, prior, posterior and predictive
distributions, marginal likelihood and Bayes theorem. Key tools and techniques introduced include Markov chain Monte Carlo (MCMC) techniques, such as the Gibbs and Metropolis Hastings algorithms, for model estimation and model comparison and the estimation of integrals via simulation methods. Throughout the course we will implement Bayesian estimation for various models such as the traditional regression model, panel models and limited dependent variable models using the Matlab programming environment.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Explain the concepts of joint, conditional and marginal probability density functions and their relevance for Bayesian inference;
- Derive posterior density functions for common econometric models including the traditional regression model, discrete outcome models and panel models;
- Explain the relevance of Markov chain Monte Carlo techniques for Bayesian inference;
- Program Gibbs samplers and Metropolis-Hastings algorithms for a number of models including the traditional regression model, discrete outcome and panel models;
- Interpret results from Bayesian inference; and
- Estimate marginal likelihoods for model comparison.
Generic skills
On successful completion of this subject, students should have improved the following generic skills:
- Evaluation of ideas, views and evidence;
- Synthesis of ideas, views and evidence;
- Strategic thinking;
- Critical thinking;
- Application of theory to economic policy and business decision making;
- Summary and interpretation of information;
- Application of Windows software;
- Using and designing computer programs;
- Statistical reasoning;
- Problem solving skills;
- Collaborative learning and teamwork;
- Written communication; and
- Oral communication.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
ECOM40006 Econometric Techniques / ECOM90013 Econometric Techniques
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| ECOM40006 | Econometrics 3 | Semester 1 (On Campus - Parkville) |
12.5 |
| ECOM90013 | Econometrics 3 | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
ECOM40002 Bayesian Econometrics
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| ECOM40002 | Bayesian Econometrics | Not available in 2020 |
12.5 |
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
| Description | Timing | Percentage |
|---|---|---|
Assignment 1 approx. 8-10 pages (not including computer code in matlab)
| From Week 6 to Week 12 | 15% |
Assignment 2 approx. 8-10 pages (not including computer code in matlab)
| From Week 6 to Week 12 | 15% |
Assignment 3
| From Week 6 to Week 12 | 10% |
End-of-semester examination
| During the examination period | 60% |
Last updated: 3 November 2022
Dates & times
Not available in 2020
Time commitment details
Estimated total time commitment of 170 hours per semester
Last updated: 3 November 2022
Further information
- Texts
- Subject notes
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Economics Course Master of Applied Econometrics Course Master of Commerce (Actuarial Science) - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 3 November 2022