This unit entry is for students who completed this unit in 2016 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.

ETC5410 - Bayesian Time Series Econometrics

6 points, SCA Band 3, 0.125 EFTSL

Postgraduate - Unit

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

Faculty

Business and Economics

Organisational Unit

Department of Econometrics and Business Statistics

Coordinator(s)

Professor Gael Martin & Associate Professor Catherine Forbes

Offered

Clayton

First semester 2016 (Day)

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, Jeffreys 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:

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

Assessment

Within semester assessment: 40%
Examination: 60%

Workload requirements

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

Chief examiner(s)

Professor Gael Martin

Prerequisites

ETC3400, ETC5340 or equivalent.

Prohibitions

ETC4541