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.
Department of Econometrics and Business Statistics
Professor Gael Martin
Associate Professor Catherine Forbes
- First semester 2018 (On-campus)
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.
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.
Within semester assessment: 40% + Examination: 60%
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.
See also Unit timetable information