Undergraduate, Postgraduate - UnitETC4541 - Bayesian Time Series Econometrics

This unit entry is for students who completed this unit in 2015 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.

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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.

Level	Undergraduate, Postgraduate
Faculty	Faculty of Business and Economics
Organisational Unit	Department of Econometrics and Business Statistics
Offered	Clayton Second semester 2015 (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 objective, Jeffreys and subjective 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 or equivalent

Prohibitions

ETC5410