units

ETC5410

Faculty of Business and Economics

Postgraduate - Unit

This unit entry is for students who completed this unit in 2014 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

print version

6 points, SCA Band 3, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

LevelPostgraduate
FacultyFaculty of Business and Economics
Organisational UnitDepartment of Econometrics and Business Statistics
OfferedClayton Second semester 2014 (Day)
Coordinator(s)Professor Gael Martin & Associate Professor Catherine Forbes

Synopsis

This unit introduces students to both foundational and methodological aspects of Bayesian econometrics. Topics covered include a review of the philosophical and probabilistic foundations of Bayesian inference; the contrast between the Bayesian and frequentist (or classical) statistical paradigms; the use of prior information via the specification of subjective, Jeffrey's and conjugate prior distributions; Bayesian linear regression; the use of simulation techniques in Bayesian inference, including Markov chain Monte Carlo algorithms; Bayesian analysis of Gaussian and non-Gaussian time series econometric models, including state space models; and the Kalman filter as a Bayesian updating rule.

Outcomes

The learning goals associated with this unit are to:

  1. appreciate the importance of Bayesian statistical techniques in econometric research and understand the differences between the Bayesian and frequentist statistical paradigms
  2. acquire the skills necessary to derive Bayesian results analytically, in simple models
  3. demonstrate an understanding of simulation methods and be able to implement these methods in empirically realistic econometric models
  4. understand the Kalman filter and its role in the Bayesian inference of linear time series models.

Assessment

Within semester assessment: 40%
Examination: 60%

Chief examiner(s)

Workload requirements

3 hours per week

Prerequisites

ETC3400 or equivalent.

Prohibitions