units

ETF5400

Faculty of Business and Economics

Postgraduate - Unit

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.

print version

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.

LevelPostgraduate
FacultyFaculty of Business and Economics
Organisational UnitDepartment of Econometrics and Business Statistics
OfferedNot offered in 2014
Coordinator(s)Professor Mervyn Silvapulle

Synopsis

The topics covered in this unit would be

  1. invaluable for any student intending to work in applied econometrics, and
  2. essential to understand journal articles in econometrics. This unit introduces some of the essentials to develop a working knowledge of econometrics for large samples. The topics covered include, weak law of large numbers, multivariate central limit theorem, large sample properties of the least squares estimator in the linear model, large sample properties of maximum likelihood estimators, and applications of these to some econometric models used in applied econometric research.

Outcomes

The learning goals associated with this unit are to:

  1. define different models used in econometrics and statistics
  2. compare different methods of estimating and testing econometric models
  3. recommend suitable methods of inference
  4. evaluate different methods of inference for econometric models
  5. summarise the advantages and disadvantages of various methods of inference.

Assessment

Within semester assessment: 100%

Chief examiner(s)

Workload requirements

Minimum total expected workload equals 144 hours per semester

Prerequisites

Students must be enrolled in course code 3816 or 3822 or 3194 or 4412 or be granted permission. It is recommended that students should have a high level of familiarity with the topics covered in ETF2100 and ETF2700.