Course coordinator
Dr Lizi Yu
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
Theory of general linear model-topics include: least squares, generalised method of moments & maximum likelihood estimators under iid, auto-correlated & heteroskedastic error specifications.
The purpose of this course is to provide the theoretical background to many econometric techniques covered in ECON7310.ﾠIt deals with the theory behind the techniques rather than the implementation of the techniques.ﾠIt also covers material on testing, generalised method of moments, and maximum likelihood estimation not encountered in ECON7310.ﾠMatrix algebra is an important mathematical tool that is used throughout this course.ﾠThe course has been structured so that you can learn the necessary matrix algebra as an integral part of the material. Lectures will present theoretical concepts in detail.
Course requirements
Assumed background
Introductory linear algebra and Statistical Theory.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON3320 or 7321
Incompatible
You can't enrol in this course if you've already completed the following:
ECON3310 + 3330
Course contact
School enquiries
Student Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Dr Lizi Yu
Tutor
Mr Chunsheng Zhang
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1.
Tutorials commence in Week 2
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
· Public Holidays: Wed 13 August (Royal Queensland Show Holiday), Mon 6 October (King’s Birthday public holiday).
· Mid-Semester Break: 29 September – 3 October. Semester 2 classes recommence on Tue 7 October.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.
Aims and outcomes
The purpose of this course is to provide the theoretical background to many econometric techniques covered in ECON2300.ﾠIt deals with the theory behind the techniques rather than the implementation of the techniques.ﾠIt also covers material on testing, generalised method of moments, and maximum likelihood estimation not encountered in ECON2300.ﾠMatrix algebra is an important mathematical tool that is used throughout this course.ﾠThe course has been structured so that you can learn the necessary matrix algebra as an integral part of the material.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain matrix algebra proficiently to derive properties of (and compute) various econometric estimators.
LO2.
Evaluate the theory behind the General Linear Regression Model.
LO3.
Explain the numerical and statistical properties of the OLS, GLS and IV estimators.
LO4.
Demonstrate the finite sample and asymptotic properties of the OLS, GLS and IV estimators.
LO5.
Use statistical packages such as R-Studio to compute the various estimators on real world datasets.
LO6.
Examine the learned theory and methods to the real world.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
In-Person Quizzes
|
60% Each quiz is out of 20. |
In lecture 21/08/2025 12:50 pm In lecture 11/09/2025 12:50 pm In lecture 16/10/2025 12:50 pm |
| Computer Code, Project |
Research Project 1
|
20% The project is 20 out of 20. |
24/09/2025 4:00 pm
Details of the Research Project 1 will be posted on the Blackboard site by Week 7 Friday (12/09) 9am. |
| Computer Code, Project |
Research Project 2
|
20% The project is 20 out of 20. |
10/11/2025 4:00 pm
Details of the Research Project 2 will be posted on the Blackboard site by Week 12 Friday (24/09) 9am. |
Assessment details
In-Person Quizzes
- Identity Verified
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 60% Each quiz is out of 20.
- Due date
In lecture 21/08/2025 12:50 pm
In lecture 11/09/2025 12:50 pm
In lecture 16/10/2025 12:50 pm
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
This assessment task is to be completed in-person. Student IDs are mandatory. Students must present a valid photo ID card to be admitted into the room for the quiz, i.e. a university-issued ID card, a current passport, or current Australian driver’s licence or government ID card.
Students should be at the venue for your quiz on time in the weeks of assessment. If you arrive late, you will not receive additional time to complete the quiz.
The 50-minute quizzes will contain MCQs, scenarios (sequential series of MCQs), and short answers. Incorrect answers will not be subject to penalties.
The first quiz will focus on all the material taught up to Lecture 3; the second will focus on all the material taught up to Lecture 6; the third will focus on all the materials taught up to Lecture 10.
This assessment is closed-book and the use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
50-minute in-person quizzes during the lecture time.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 3 days. Extensions are given in multiples of 24 hours.
Extensions are a one-time opportunity only for this assessment.
The date and time for the replacement quiz are set by the course coordinator. Students must make themselves available to sit the quiz at the scheduled extension time. There are no extensions on extensions for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Research Project 1
- Online
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 20% The project is 20 out of 20.
- Due date
24/09/2025 4:00 pm
Details of the Research Project 1 will be posted on the Blackboard site by Week 7 Friday (12/09) 9am.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Artificial Intelligence (AI) and Machine Translation (MT) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task.. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit via Blackboard by the due date and time.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Research Project 2
- Online
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 20% The project is 20 out of 20.
- Due date
10/11/2025 4:00 pm
Details of the Research Project 2 will be posted on the Blackboard site by Week 12 Friday (24/09) 9am.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Artificial Intelligence (AI) and Machine Translation (MT) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task.. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit via Blackboard by the due date and time.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student’s final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
ﾠ
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
Abadir M.A., Magnus J.R., "Matrix Algebra", Cambridge
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (28 Jul - 03 Aug) |
Lecture |
Introduction + Review on Linear Algebra and Regression Models Review of statistical concepts and matrix algebra. Introduction to the linear regression model. Learning outcomes: L01 |
Week 2 (04 Aug - 10 Aug) |
Lecture |
Geometry of Linear Regression 1 Geometry of vector spaces; geometry of OLS Learning outcomes: L02, L03 |
Week 3 (11 Aug - 17 Aug) |
Lecture |
Geometry of Linear Regression 2 More on linear regression Learning outcomes: L02, L03 |
Week 4 (18 Aug - 24 Aug) |
Lecture |
Quiz 1 + Statistical Properties of OLS 1 No lecture or tutorial this week Learning outcomes: L02, L03, L04 |
Week 5 (25 Aug - 31 Aug) |
Lecture |
Statistical Properties of OLS 2 More on the statistical properties of OLS. Learning outcomes: L04, L05, L06 |
Week 6 (01 Sep - 07 Sep) |
Lecture |
Hypothesis Testing Some common distribution. Exact and large sample tests. Learning outcomes: L04, L05, L06 |
Week 7 (08 Sep - 14 Sep) |
Lecture |
Quiz 2 + Confidence Interval More on hypothesis testing and confidence intervals. Learning outcomes: L04, L05, L06 |
Week 8 (15 Sep - 21 Sep) |
Lecture |
Generalized Least Squares 1 GLS estimator; FGLS. Learning outcomes: L02, L03, L04 |
Week 9 (22 Sep - 28 Sep) |
Lecture |
Generalized Least Squares 2 Learning outcomes: L02, L03, L04 |
Week 10 (06 Oct - 12 Oct) |
Lecture |
Instrumental Variable Regression Method of Moments Learning outcomes: L03, L04, L05, L06 |
Week 11 (13 Oct - 19 Oct) |
Lecture |
Quiz 3 + Maximum Likelihood Estimation 1 Learning outcomes: L03, L04, L05, L06 |
Week 12 (20 Oct - 26 Oct) |
Lecture |
Maximum Likelihood Estimation 2 Learning outcomes: L03, L04, L05, L06 |
Week 13 (27 Oct - 02 Nov) |
Lecture |
Revision Exam revision. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.