6 points, SCA Band 3, 0.125 EFTSL
Undergraduate, Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
- Second semester 2017 (Day)
This unit involves a critical review of recent empirical work in econometrics. The topics to be covered include i) the specification and estimation of systems of consumer demand equations and producer demand and supply equations and extensions of these methods to other areas; and ii) structural vector autoregressions and error correction models, together with some practical applications that address macroeconomic issues. On completion of this unit students should be familiar with recent developments in these fields and have developed the skills to undertake empirical work. Further, students should be able to critically evaluate empirical work and related policy implications.
The learning goals associated with this unit are to:
- specify the structure of consumer demand systems
- use duality theory to generalise these specifications
- understand the use of appropriate specific estimation techniques
- extend these ideas to producer demand and supply models
- consider a number of empirical applications, and derive policy implications
- become competent in handling demand and production data using econometric software
- specify, estimate and interpret growth regressions
- understand the modelling of trends, cycles and structural breaks
- specify, estimate and interpret structural vector autoregressions and error correction 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