Bayesian Econometrics

This subject is not offered in 2014.

Estimated total time commitment of 120 hours per semester

ECOM40006 Econometric Techniques / ECOM90013 Econometric Techniques

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ECOM40002 Bayesian Econometrics

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/

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.

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, the seemingly unrelated regression model, probit and tobit models and some time series 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 seemingly unrelated regressions models and the ordered probit model;
• Interpret results from Bayesian inference;
• Explain the concept of model averaging;
• Estimate marginal likelihoods and use these estimates to model average a simple problem.

• Two hour end-of-semester examination (60%); and
• Three assignments due between weeks 6 and 12: Assignment 1 (15%) approx. 8-10 pages (not including computer code in matlab); Assignment 2 (15%) approx. 8-10 pages (not including computer code in matlab); Assignment 3 (10%) approx. 8-10 pages

You will be advised of prescribed texts by your lecturer.

This subject is not available as a breadth subject.

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
• Oral communication

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