Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject is not offered in 2015.
|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 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 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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