Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject is not offered in 2016.
|Time Commitment:||Contact Hours: Two 1.5-hour lectures per week (Semester 2) |
Total Time Commitment: Not available
Admission into BH-COM or BH-ARTS (Economics) and
Study Period Commencement:
|Recommended Background Knowledge:|| |
Please refer to Prerequisites and Corequisites.
|Non Allowed Subjects:|
|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: http://services.unimelb.edu.au/disability
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.
On successful completion of this subject students should be able to:
A 2-hour end-of-semester examination (60%) and up to three assignments totalling 5000 words due between weeks 6 and 12 (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|
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