Course coordinator
Dr Fu Ouyang
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
This course concentrates on mainstream models and estimation and inference methods that are widely used in most empirical investigations in applied microeconomics. The course has a topics-based structure, and theory and applications are closely integrated. Topics include parametric and semi-parametric estimation methods applied to cross-section and panel data; treatment evaluation; models of cross-sectional dependence; quantile and mixture regressions; density estimation; Bayesian regression analysis.
This course has a strong practical focus and is designed to provide students with the advanced econometrics skills needed to complete quantitative research at the postgraduate level.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON7321 or 7333 or 7350 or 7360
Incompatible
You can't enrol in this course if you've already completed the following:
ECON6300
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
The aims of this course are to:
- provide students with a soundﾠunderstanding of aﾠrange of modern econometric models and related methods of estimation and inference;
- give students practical experience of econometric data analysis using mainstreamﾠsoftware and real and simulated data; and
- give students the skills to critically evaluate published econometric studies.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Outline the theoretical foundations of workhorse econometric methods.
LO2.
Apply econometric methods to real-world data and learn the correct way to report and interpret empirical results.
LO3.
Choose and implement appropriate econometric techniques to conduct estimation, prediction, and statistical inference.
LO4.
Examine the internal and external validities of econometric models applying to specific empirical problems.
LO5.
Implement econometric analysis using software packages like R.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Problem Set 1 | 25% |
2/04/2026 4:59 pm |
| Tutorial/ Problem Set | Problem Set 2 | 25% |
15/05/2026 4:59 pm |
| Examination |
End-of-Semester Exam
|
50% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
Problem Set 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 25%
- Due date
2/04/2026 4:59 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This assessment task involves analytical and data-driven problems. It provides a solid foundation in econometric modeling and computational research. Students will apply a variety of estimation methods while learning to navigate different data structures. By linking theoretical concepts with practical R programming, students will practice and learn how to: 1. Identify causal effects, 2. Conduct estimation and statistical inference, and 3. Manage real-world data.
Artificial Intelligence (AI) and Machine Translation (MT) provide 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 the use of generative AI or MT may constitute student misconduct under the Student Code of Conduct.
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.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
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.
Problem Set 2
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 25%
- Due date
15/05/2026 4:59 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This assessment task involves analytical and data-driven problems. It provides a solid foundation in econometric modeling and computational research. Students will apply a variety of estimation methods while learning to navigate different data structures. By linking theoretical concepts with practical R programming, students will practice and learn how to: 1. Identify causal effects, 2. Conduct estimation and statistical inference, and 3. Manage real-world data.
Artificial Intelligence (AI) and Machine Translation (MT) provide 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 the use of generative AI or MT may constitute student misconduct under the Student Code of Conduct.
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.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
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
- 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, L04
Task description
The final exam is comprehensive and will cover all topics discussed throughout the course. Additional details will be provided during the last lecture.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI 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 | Closed book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten or typed notes, double sided, is permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
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.
REFERENCING AND CITING
Assignments must be substantially your own work. If you wish to report another author’s point of view you should do so in your own words, and properly cite the reference in accordance with the school style. Direct quotations should be used sparingly, form a small part of your work, and must be placed in quotation marks and duly referenced.
• Any material taken from texts and other references, including electronic resources, CD‐ROMS, and the Internet, must be acknowledged using the accepted School style.
• Students are encouraged to discuss issues that arise in this course together. However, the written work you submit must be entirely your own. Similarly, you must not help another student to cheat by lending assignments (present or past).
• For more information on referencing styles, visit the library or seeﾠhttps://guides.library.uq.edu.au/referencing
• If you do not reference the materials used in your assignment correctly, you could be found guilty of academic misconduct. Please see this link for more information:ﾠhttps://ppl.app.uq.edu.au/content/3.60.04-student-integrity-and-misconduct
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.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Econometrics | Hansen, B. (2022), Econometrics, Princeton University Press. |
Recommended
| Item | Description | Further Requirement |
|---|---|---|
| Econometric Analysis | Greene, W.H. (2020), Econometric Analysis, 6th edition, Pearson/ Prentice Hall. | |
| Econometric Analysis of Cross Section and Panel Data | Wooldridge, J.W. (2011), Econometric Analysis of Cross Section and Panel Data, 2nd edition, MIT Press. | |
| Discrete Choice Methods with Simulation | Train, K.E. (2003), Discrete Choice Methods with Simulation, Cambridge University Press | |
| Microeconometrics : Methods and Applications | Cameron, A.C., and P.K. Trivedi (2005), Microeconometrics: Methods and Applications, Cambridge University Press. |
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 |
Course Introduction and Math Review course introduction, Matrix algebra, review of elementary probability and statistics Learning outcomes: L01, L02, L03, L04 |
Week 2 (02 Mar - 08 Mar) |
Lecture |
Review of Multiple Regression and M-estimation Review matrix treatment of multiple regression; Gauss-Markov Theorem and assumptions; conditional prediction; loss function; M-estimation; causal vs. non causal relations; examples. Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 1 Review and practice the materials covered in Lecture 1. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 3 (09 Mar - 15 Mar) |
Lecture |
Maximum Likelihood Estimation Basic likelihood concepts; score functions; computation of MLE; large sample properties; examples from univariate and regression models; likelihood-based inference. Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 2 Review and practice the materials covered in Lecture 2. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 4 (16 Mar - 22 Mar) |
Lecture |
GMM Basics and Extensions Simultaneous equations framework. Essential GMM Motivation; the Analogy Principle; causal parameters; simultaneous equations; IV estimation; GMM extensions; large sample properties Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 3 Review and practice the materials covered in Lecture 3. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 5 (23 Mar - 29 Mar) |
Lecture |
Linear Panel Data Models A Advantages of panel data; basics of linear panel models; pooled, random effects and fixed effect models; target parameters and estimation by GLS; applications. Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 4 Review and practice the materials covered in Lecture 4. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 6 (30 Mar - 05 Apr) |
Lecture |
Linear Panel Data Models B Extensions of basic models; types of exogeneity; endogenous regressors; dynamic models; GMM methods; application to MABEL data. Friday, April 3rd, is Good Friday, a public holiday, so there will be no classroom lectures or consultation sessions that day. A recorded lecture will be uploaded to Ultra for students to access. Students who typically attend tutorials on this day are encouraged to attend an alternative tutorial session for this week only. Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 5 Review and practice the materials covered in Lecture 5. Learning outcomes: L01, L02, L03, L04, L05 |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
In-Semester Break - No lecture, no tutorials, no consultations |
Week 7 (13 Apr - 19 Apr) |
Lecture |
Simulation-based Estimation and Inference Computer-intensive methods for estimation and inference; simulation-based MLE and GMM; bootstrap standard errors; applications to panel models. Learning outcomes: L01, L02, L03, L04 |
Tutorial |
Tutorial 6 Review and practice the materials covered in Lecture 6. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 8 (20 Apr - 26 Apr) |
Lecture |
Quantile Regression Conditional quantiles (CQ); semiparametric models; marginal quantiles; MAD and CQ estimation; advantages of non separable heterogeneous responses; treatment effects. 18 April is a public holiday. No class, tutorial, or consultation session will be held that day. Learning outcomes: L01, L03, L04 |
Tutorial |
Tutorial 7 Review and practice the materials covered in Lecture 7. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 9 (27 Apr - 03 May) |
Lecture |
Hypothesis Tests Key concepts related to hypothesis tests, Wald, criterion-based, and score tests. Learning outcomes: L01, L02, L03, L05 |
Tutorial |
Tutorial 8 Review and practice the materials covered in Lecture 8. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 10 (04 May - 10 May) |
Lecture |
Non-parametric and Flexible Parametric Methods A Kernel density and regression; mixture models; kernel regression; mixture of normals; inference on mixture models 5 May is a public holiday. No class, tutorial, or consultation session will be held that day. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 9 Review and practice the materials covered in Lecture 9. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 11 (11 May - 17 May) |
Lecture |
Non-parametric and Flexible Parametric Methods B Kernel density and kernel regression; mixture of normals; inference on mixture models; relationship to semiparametric models; random effects and mixed models. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 10 Review and practice the materials covered in Lecture 10. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 (18 May - 24 May) |
Lecture |
Big Data and Machine Learning High dimensional regression; Ridge regression; LASSO; penalty variable selection; LASSO IV; double/debiased machine learning. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 11 Review and practice the materials covered in Lecture 11. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 13 (25 May - 31 May) |
Lecture |
Review Lecture Review lecture Learning outcomes: L01 |
Tutorial |
Tutorial 12 Review and practice the materials covered in Lecture 12. Learning outcomes: L01, L02, L03, L04, 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
- AI for Assessment Guide
- Recording of Teaching Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.