BEX5420 Microeconometrics
Faculty of Business and Economics
BEX5420 Microeconometrics is a level 5, 0-credit-point, postgraduate unit from the Faculty of Business and Economics, offered in 2023 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 0
- Offered in 2023
- Semester 1
- Clayton
- Assessment
- Exam 60%
- and 1 other task
- Workload
- 144 hours
- per semester
The 2027 handbook has no page for BEX5420. This is its 2023 entry, the latest one.
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Requisites
Before BEX5420
Prohibitions
You can't enrol if you have passed any of these.
After BEX5420
No unit lists BEX5420 as a prerequisite in the 2023 handbook.
Enrolment rules
You must be enrolled in course 0029 or 3194 to undertake this unit.
Equivalent units
The same content under another code. Only one of them counts.
Overview
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 you to gain hands-on experience and computation skills for analysing large scale micro datasets. The computing package used for the unit is STATA.
Offerings in 2023
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Within semester assessment40%
- ExaminationThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
become familiar with typical features and structures of micro datasets
- 2
identify microeconometric models suitable for given micro datasets and given research objectives
- 3
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
- 4
summarise and present key model results and measures of interest in tables and graphs
- 5
be comfortable and proficient with the use of STATA software to manage and analyse large datasets
- 6
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.
Workload and teaching
- Workshops36 hours
- Teaching approachActive learning
- Teaching approachProblem-based learning
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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Contacts
- Chief Examiners
- Professor Xueyan Zhao
Common questions
What are the prerequisites for BEX5420?
BEX5420 has no prerequisites, but enrolment rules apply.
When is BEX5420 offered?
In 2023, BEX5420 runs in Semester 1 at Clayton.
How much work is BEX5420?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does BEX5420 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 1 other task.