Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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 econometric theory which builds upon ECON3330. The focus will be on theoretical foundations of econometrics, including the asymptotic theory behind M-estimators and likelihood-based inference, nonparametric and semiparametric econometrics.
This course is designed for students with a strong background in both Econometrics and Statistics.ﾠThe main objective is to further the understanding of both theoretical aspects of econometrics and advanced econometric techniques by building upon the knowledge gained in previous courses. ﾠﾠ
The assessments for the course will involve students using the econometric foundations and techniques they learn in class.ﾠ
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
Shared Teaching Activities: This course will share teaching activities with ECON6310.
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.
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. ﾠ
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 framework for deriving asymptotic results.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Exercises 1 | 34% |
30/08/2024 1:00 pm |
| Tutorial/ Problem Set | Exercises 2 | 33% |
4/10/2024 1:00 pm |
| Tutorial/ Problem Set | Exercises 3 | 33% |
25/10/2024 1:00 pm |
Assessment details
Exercises 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 34%
- Due date
30/08/2024 1:00 pm
- Learning outcomes
- L01, L04, L05
Task description
The assignment will consist of a set of theoretical exercises. Answers including a detailed derivation of each of the solutions to the questions need to be provided.
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
Assignments can be submitted directly to the Lecturer.
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.
Exercises 2
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
4/10/2024 1:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignment will consist of a set of theoretical exercises. Answers including a detailed derivation of each of the solutions to the questions need to be provided.
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
Assignments can be submitted directly to the Lecturer.
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.
Exercises 3
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
25/10/2024 1:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignment will consist of a set of theoretical exercises. Answers including a detailed derivation of each of the solutions to the questions need to be provided.
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
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.
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 Assessment Related Policies & Guidelines. Please refer to the link to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIMﾠif you have not already done so.
SUBMISSION OF ASSIGNMENTS
All assignments must be submitted by the due date and time stated in the course profile. For this course, students are required to submit assignments to the Lecturer.
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 5 |
Lecture |
Advanced Econometric Theory Topics 1 Understand the foundations behind different advanced econometric concepts. Learning outcomes: L01, L02, L03 |
Week 4 (12 Aug - 18 Aug) |
No student involvement (Breaks, information) |
Ekka public holiday No lecture. |
Multiple weeks From Week 5 To Week 9 |
Lecture |
Advanced Econometric Theory Topics 2 Learning outcomes: L02, L03, L04, L05 |
Multiple weeks From Week 9 To Week 13 |
Lecture |
Advanced Econometric Theory Topics 3 Learning outcomes: L02, L03 |
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid-Semester Break |
Week 11 (07 Oct - 13 Oct) |
No student involvement (Breaks, information) |
King's Birthday public holiday |
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.