Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- 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, autocorrelated & heteroskedastic error specifications.
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. 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:
ECON2105 or 3320
Incompatible
You can't enrol in this course if you've already completed the following:
ECON3310
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
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Tutorials will commence in Week 2.ﾠ
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 29 September. Semester 2 classes recommence Mon 30 September.
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.
Apply matrix algebra proficiently to derive properties of (and compute) various econometric estimators.
LO2.
Explain the theory behind the General Linear Regression Model.
LO3.
Derive and explain the numerical and statistical properties of the OLS, GLS and IV estimators.
LO4.
Understand 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.
Apply the learned theory and methods to the real world.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Quiz |
Problem Solving Exercises
|
60% (3 sets of Quizzes@20%) |
Problem Solving Exercise 1. Problem Solving Exercise 2. Problem Solving Exercise 3.
Online Periodic Assessments Throughout the Semester. Further details will be communicated via Blackboard. |
| Examination |
Final Exam
|
40% Final Exam is 40% of the total mark. |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
Assessment details
Problem Solving Exercises
- Online
- Mode
- Written
- Category
- Computer Code, Quiz
- Weight
- 60% (3 sets of Quizzes@20%)
- Due date
Problem Solving Exercise 1.
Week 7 - Week 9Problem Solving Exercise 2.
Week 9 - Week 11Problem Solving Exercise 3.
Week 11 - Week 13Online Periodic Assessments Throughout the Semester. Further details will be communicated via Blackboard.
- Other conditions
- Student specific, Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Online Quizzes and R-exercises.
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
Online via Blackboard. No late submission will be accepted, since solutions may be released after the due date.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
No late submission will be accepted. CML access is blocked after the due date and time.
Final Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40% Final Exam is 40% of the total mark.
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Student specific, Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Final Exam.
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.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Open Book examination |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
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%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies & Guidelines. Please refer to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIM if you have not already done so.
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
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Abadir K.M., Magnus J.R. (2005), "Matrix Algebra", Cambridge University Press. Available online at the UQ Library
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 (22 Jul - 28 Jul) |
Lecture |
Linear Algebra and Regression Review of statistical concepts and matrix algebra. Learning outcomes: L01 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
Linear Algebra and Regression Review of statistical concepts and matrix algebra. Learning outcomes: L01 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
Geometry of Linear Regression 1 Geometry of vector spaces; geometry of OLS Learning outcomes: L01, L02 |
Week 4 (12 Aug - 18 Aug) |
Lecture |
Statistical properties of OLS 1 Statistical Properties of OLS Learning outcomes: L02, L03 |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Statistical Properties of OLS 2 More on the statistical properties of OLS. Learning outcomes: L04 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
Hypothesis Testing and Confidence Intervals 1 Some common distribution. Exact and large sample tests. Learning outcomes: L04 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
Hypothesis Testing and Confidence Intervals 2 More on hypothesis testing and confidence intervals. Learning outcomes: L04, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Generalized Least Squares (GLS) GLS estimator; FGLS; Panel Data Learning outcomes: L03 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
GLS; FGLS; Panel Data GLS estimator; FGLS; Panel Data Learning outcomes: L03 |
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid-Semester Break No lectures or tutorials during the break. |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Instrumental Variables IV Estimation Learning outcomes: L03 |
Week 11 (07 Oct - 13 Oct) |
Lecture |
Maximum Likelihood Estimator ML Estimation Learning outcomes: L04, L06 |
Week 12 (14 Oct - 20 Oct) |
Lecture |
Limited Dependent Variable Models Logit and Probit Learning outcomes: L04 |
Week 13 (21 Oct - 27 Oct) |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.