Postgraduate - UnitETC5410 - Bayesian Time Series Econometrics

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.

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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, or view unit timetables.

Level	Postgraduate
Faculty	Faculty of Business and Economics
Organisational Unit	Department of Econometrics and Business Statistics
Offered	Clayton 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:

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 the Bayesian inference of linear time series models.

Assessment

Within semester assessment: 40%
Examination: 60%

Chief examiner(s)

Associate Professor Catherine Forbes

Workload requirements

3 hours per week

Prerequisites

ETC3400 or equivalent.

Prohibitions

ETC4541