Course coordinator
Dr Haishan Yuan
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
This course provides a rigorous introduction to the statistical methods which are fundamental to further study in econometrics and mathematical economics. It aims to provide students with a formal understanding of key results encountered in elementary courses such as Introductory Econometrics (ECON2300) and to prepare students for more advanced courses such as Econometric Theory (ECON3330). Topics include probability theory, concentration inequalities, moment generating functions, estimation theory (Ordinary Least Squares, Maximum Likelihood, Method of Moments), notions of convergence, asymptotic properties of estimators, laws of large numbers, central limit theorem, hypothesis testing and inference.
This course provides a rigorous introduction to the statistical methods which are fundamental to further study in econometrics and mathematical economics. It aims to provide students with a formal understanding of key results encountered in elementary courses such as Introductory Econometrics and to prepare students for more advanced courses such as Econometric Theory. The course differs from the type of course that would be taught in a Science program in that it is specially targeted to deal with statistical theory relevant to the empirical modelling of observational (non-experimental) data, which is the type of data which economists and social scientists deal with regularly.ﾠTopics include probability theory, concentration inequalities, moment generating functions, estimation theory (Ordinary Least Squares, Maximum Likelihood, Method of Moments), notions of convergence, asymptotic properties of estimators, laws of large numbers, central limit theorem, hypothesis testing and inference.ﾠ
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1050 + 1310
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
ECON2300
Incompatible
You can't enrol in this course if you've already completed the following:
ECON3320
Course contact
School enquiries
School 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 toﾠenquiries@economics.uq.edu.au.ﾠ
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
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: Fri 3 April (Good Friday), Mon 4 May (Labour Day).
- Mid-Semester Break: 6 April - 10 April. Semester 1 classes recommence on Mon 13 April.
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
This course aims to provide students with a formal understanding of key results encountered in elementary courses such as Introductory Econometrics (ECON2300),ﾠand to prepare students for more advanced courses such as Econometric Theory (ECON3330).
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply fundamental statistical concepts to derive key statistical results such as Central Limit Theorem.
LO2.
Analyse the properties of modern econometric methods.
LO3.
Evaluate modern econometric methods in light of their underlying statistical assumptions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set |
Assignment 1
|
25% |
24/04/2026 3:00 pm |
| Tutorial/ Problem Set |
Assignment 2
|
25% |
22/05/2026 3:00 pm |
| Examination |
End-of-semester Exam
|
50% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
Assignment 1
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 25%
- Due date
24/04/2026 3:00 pm
- Learning outcomes
- L01
Task description
Analytical problems on lectures 1-5.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Electronic submissions via Blackboard of a single pdf document with all relevant materials.
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.
Assignment 2
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 25%
- Due date
22/05/2026 3:00 pm
- Learning outcomes
- L02, L03
Task description
Analytical problems on lectures 6-10.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Electronic submissions via Blackboard of a single pdf document with all relevant materials.
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.
End-of-semester Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Other conditions
- Secure.
- Learning outcomes
- L01, L02, L03
Task description
The end-of-semester exam is designed to cover all learning objectives and to test both depth and breadth of students’ knowledge relevant to the course. The end-of-semester exam will cover the course material (including lectures and exercises) of the whole semester.
This assessment task is to be completed in-person. 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.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| 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
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.
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 (23 Feb - 01 Mar) |
Lecture |
Lecture 1 Course introduction, Probability theory. Learning outcomes: L01 |
Week 2 (02 Mar - 08 Mar) |
Tutorial |
Tutorial 1 Covers lecture 1 material. Learning outcomes: L01 |
Lecture |
Lecture 2 Laws of probability, random variables. Learning outcomes: L01 |
|
Week 3 (09 Mar - 15 Mar) |
Tutorial |
Tutorial 2 Covers lecture 2 material. Learning outcomes: L01 |
Lecture |
Lecture 3 Transformations of random variables, moments, probability inequalities. Learning outcomes: L01 |
|
Week 4 (16 Mar - 22 Mar) |
Tutorial |
Tutorial 3 Covers lecture 3 material. Learning outcomes: L01 |
Lecture |
Lecture 4 Moment generating function, Bernoulli distribution, Binomial distribution. Learning outcomes: L01 |
|
Week 5 (23 Mar - 29 Mar) |
Tutorial |
Tutorial 4 Covers lecture 4 material. Learning outcomes: L01 |
Lecture |
Lecture 5 Poisson, Exponential, Uniform and Normal distributions. Learning outcomes: L01 |
|
Week 6 (30 Mar - 05 Apr) |
Tutorial |
Tutorial 5 Covers lecture 5 material. Students who would normally attend a Friday tutorial are invited to attend another tutorial for this week only due to Good Friday (3 April). Learning outcomes: L01 |
Lecture |
Lecture 6 Gamma, Chi-squared, t and F distributions. Learning outcomes: L01 |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-sem break |
Week 7 (13 Apr - 19 Apr) |
Tutorial |
Tutorial 6 Covers lecture 6 material. Learning outcomes: L01 |
Lecture |
Lecture 7 Sampling, introduction to inference, method of moments. Learning outcomes: L02 |
|
Week 8 (20 Apr - 26 Apr) |
Tutorial |
Tutorial 7 Covers lecture 7 material. Learning outcomes: L02 |
Lecture |
Lecture 8 Ordinary Least Squares, Maximum Likelihood, Cramer-Rao lower bound. Learning outcomes: L02, L03 |
|
Week 9 (27 Apr - 03 May) |
Tutorial |
Tutorial 8 Covers lecture 8 material. Learning outcomes: L02, L03 |
Lecture |
Lecture 9 Asymptotic theory, convergence to a constant (Laws of Large Numbers), convergence to a random variable (Central Limit Theorem). Learning outcomes: L02, L03 |
|
Week 10 (04 May - 10 May) |
Tutorial |
Tutorial 9 Covers lecture 9 material. Learning outcomes: L02, L03 |
Lecture |
Lecture 10 Confidence intervals, hypothesis testing. Learning outcomes: L02, L03 |
|
Week 11 (11 May - 17 May) |
Tutorial |
Tutorial 10 Covers lecture 10 material. Learning outcomes: L02, L03 |
Lecture |
Lecture 11 Size and power of a test, most powerful test, Likelihood ratio test, Wald test. Learning outcomes: L02, L03 |
|
Week 12 (18 May - 24 May) |
Tutorial |
Tutorial 11 Covers lecture 11 material. Learning outcomes: L02, L03 |
Lecture |
Spillover lecture (if needed) Learning outcomes: L01, L02, L03 |
|
Week 13 (25 May - 31 May) |
Lecture |
Revision lecture Learning outcomes: L01, L02, L03 |
Tutorial |
Revision tutorial Learning outcomes: L01, L02, L03 |
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
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.