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Bayesian Econometrics (ECOM90010)

Graduate courseworkPoints: 12.5Not available in 2018

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Overview

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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 Econometric Techniques
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 2024
 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

Additional details

  • 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

Last updated: 3 November 2022

Dates & times

Not available in 2018

Time commitment details

Estimated total time commitment of 170 hours per semester

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    You will be advised of prescribed texts by your lecturer.

  • Subject notes

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

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Applied Econometrics
    CourseMaster of Economics
    CourseMaster 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.

    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

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 3 November 2022