Course coordinator
Dr Mohamad Khaled
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
This is an advanced course in econometrics. The focus will be on foundations of econometric theory, including the theory behind parametric, nonparametric and semi-parametric econometrics, and its applicability to policy evaluations.
This course is designed for students interested in the econometric foundations of policy evaluation. The students will learn both econometric techniques and coding skills. The course will start with basic foundations of causal inference and will address some of the recent developments of the most popular applied econometric techniques for policy evaluation.
Course requirements
Assumed background
Elements of statistical and econometric theory.
ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON7320 or 7321 or 7331 or 7360
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON3300 or 3350 or 3360 or 7350 or 7360
Incompatible
You can't enrol in this course if you've already completed the following:
ECON6310
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 Mohamad Khaled
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
The timetable is published through the UQ Public Timetable found in the APPs section of myUQ. 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.
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.
Aims and outcomes
The main aim of this course is to provide students with a strong grounding in the theory that underpins many standard econometric methods. 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 that they may come across in their future studies.
ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the application of asymptotic results to specific econometric models.
LO2.
Examine consistency results for some econometric estimators.
LO3.
Explain the different assumptions behind some advanced econometric results.
LO4.
Explain the differences between different classes of econometric estimators.
LO5.
Develop a mathematical framework for deriving Econometric Results for Policy Evaluations.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Assignment 1 | 34% |
5/09/2025 1:00 pm |
| Tutorial/ Problem Set | Assignment 2 | 33% |
10/10/2025 1:00 pm |
| Tutorial/ Problem Set | Assignment 3 | 33% |
10/11/2025 1:00 pm |
Assessment details
Assignment 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 34%
- Due date
5/09/2025 1:00 pm
- Learning outcomes
- L01, L03, L04, L05
Task description
The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.
Artificial Intelligence (AI) and Machine Translation (MT) are 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
Assignments must be submitted in Blackboard.
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
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
10/10/2025 1:00 pm
- Learning outcomes
- L02, L03, L04, L05
Task description
The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.
Artificial Intelligence (AI) and Machine Translation (MT) are 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
Assignments must be submitted in Blackboard.
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 3
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
10/11/2025 1:00 pm
- Learning outcomes
- L03, L04, L05
Task description
The assignment will contain a set of practical coding and data analysis exercises as well as theoretical questions and will cover topics from previous lectures.
Artificial Intelligence (AI) and Machine Translation (MT) are 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
Assignments must be submitted in Blackboard.
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%.
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. 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.
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
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
Some additional reading material will be provided to students in some topics.
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 |
|---|---|---|
Multiple weeks From Week 1 To Week 4 |
Lecture |
Policy Evaluation Econometric Foundational Topics Learning outcomes: L01, L02, L03, L04, L05 |
Week 3 (11 Aug - 17 Aug) |
No student involvement (Breaks, information) |
Ekka public holiday No lecture. |
Multiple weeks From Week 5 To Week 9 |
Lecture |
Policy Evaluation Econometric Toolbox Topics Learning outcomes: L02, L03, L04, L05 |
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Semester Break |
Multiple weeks From Week 10 To Week 13 |
Lecture |
Policy Evaluation Econometric Further Topics Learning outcomes: L02, L03, L05 |
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.