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.
- First semester 2017 (Day)
This unit involves the analysis of micro-level cross-sectional and panel data to study the behaviour of individuals and other micro-units as decision makers. It studies the specification, estimation, inference and evaluation of a range of microeconometric models. These include models for discrete, count, duration, censored or truncated dependent variables and examine issues arisen from sample selection and endogenous treatment. The aim of the unit is also for students to gain hands-on experience and computation skills for analysing large scale micro datasets. The computing package used for the unit is STATA.
The learning goals associated with this unit are to:
- become familiar with typical features and structures of micro datasets
- be able to identify microeconometric models suitable for given micro datasets and given research objectives
- be able to specify, estimate, evaluate and analyse econometric models with dependent variables that are binary choices, multinomial discrete choices, durations, censored or truncated, using a given dataset
- be able to summarise and present key model results and measures of interest in tables and graphs
- be comfortable and proficient with the use of STATA software to manage and analyse large datasets
- have had hands-on experience with analysing several real world micro datasets in health, labour, finance and marketing research, through computing exercises and a written assignment.
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