This is an advanced course on econometric theory. It will provide students with grounding in the theory that underpins many standard econometric methods. It will stress fundamental ideas and general concepts so that students can draw connections between various methods presented in other courses, and learn to derive simple theoretical results from first principles and apply them to specific cases.
The course does not attempt to provide details on the theory behind all possible econometric models, but instead seeks to ensure that students gain sufficient skills to allow them to understand the key issues and hence to be able to read and understand advanced texts and journal articles on current research that they may come across in their future studies or research.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the requirements for this course, students will be able to:
• Understand and apply selected inferential methods
• Critically evaluate a range of articles in econometric theory
• Derive asymptotic or finite sample properties of specific estimators under various assumptions
• Evaluate the appropriateness of several alternative estimators in specific applications
Other Information
See the course outline on the College courses page. Outlines are uploaded as they become available.
Indicative Assessment
One assignment worth 25%, a mid semester exam worth 25% and a final exam worth 50%.
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Students taking this course are expected to commit at least 12 hours a week comprised of:• 3 hours of lectures and,
• 1 hour of tutorial, and
• 8 hours of private study (at least).
• (Recommended and Optional) Study group to be organized by students, if they wish.
Requisite and Incompatibility
Preliminary Reading
See Course Outline
Assumed Knowledge
All students should know some calculus, and have some knowledge of linear algebra, matrix algebra, probability. Understanding of maximum likelihood, least squares, simple time series models, and simple cases of laws of large numbers and central limit theorems.
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 3
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2017 | $3852 |
- International fee paying students
| Year | Fee |
|---|---|
| 2017 | $5130 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 8215 | 24 Jul 2017 | 31 Jul 2017 | 31 Aug 2017 | 27 Oct 2017 | In Person | N/A |