Bayesian Econometrics

This subject is not offered in 2016.

Admission into BH-COM or BH-ARTS (Economics) and

None

Please refer to Prerequisites and Corequisites.

Students may not gain credit for both ECOM40002 Bayesian Econometrics and ECOM90010 Bayesian Econometrics.

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:

• 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;
• Estimate marginal likelihoods for model comparison

A 2-hour end-of-semester examination (60%) and up to three assignments totalling 5000 words due between weeks 6 and 12 (40%).

You will be advised of prescribed texts by your lecturer.

This subject is not available as a breadth subject.

• 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.