6 points, SCA Band 3, 0.125 EFTSL
Undergraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
Faculty
Organisational Unit
Department of Econometrics and Business Statistics
Coordinator(s)
Unit guides
Synopsis
This unit presents econometric models and techniques that are widely used in applied econometrics. The topics covered are linear regression models with random regressors, method of moments and instrumental variables estimation; simultaneous equations models; models for time-series data; introduction to maximum likelihood estimation; models for discrete dependent variables and models for panel data. EViews computer software is used to carry out data analysis and estimation.
Outcomes
The learning goals associated with this unit are to:
- conduct statistical inference in linear regression models with random regressors using the method of moments and the instrumental variables estimators
- conduct statistical inference for simultaneous equations models
- understand the statistical properties of nonstationary macroeconomic time series data and how to model the long-run relationships among co-integrated time series
- conduct statistical inference in models with discrete dependent variables
- conduct statistical inference in panel data models.
Assessment
Within semester assessment: 40% + Examination: 60%
Workload requirements
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
Chief examiner(s)
Prerequisites
Students must have passed ETF2100 or ETC2410 or ETC3440 or equivalent or must be enrolled in Course Code 3822, 4412 or B6001.