Course coordinator
Dr Eric Eisenstat
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
This course focuses on the core models and estimation methods widely used in empirical economic studies. With a topics-based structure, it combines modern econometric theory with practical applications in economics, finance, management, and related fields. This course aims to equip students with a valuable and practical toolkit that is highly sought after in industry roles and essential for post-graduate studies where analytical skills are crucial. Students will gain hands-on experience by applying these techniques using widely used software packages. The course will cover key advanced econometric estimation and inference procedures.
Course requirements
Incompatible
You can't enrol in this course if you've already completed the following:
ECON6300
Restrictions
Enrolment restricted to students in the BEcon(Hons), BAdvFinEcon(Hons), BA(Hons - Economics)
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.
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1: Tuesday
Tutorials commence in Week 2: Tuesday
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
· Public Holidays: Fri 18 April (Good Friday), Mon 5 May (Labour Day).
· Mid-Semester Break: 21ﾠApril - 25ﾠApril. Semester 1 classes recommence on Mon 28 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:
1. Give students a thorough understanding of modern econometric models, including the methods used for estimating and inferring them.
2. Provide students with hands-on experience in analysing economic data using popular software packages and both real-world and simulated datasets.
3. Provide students with the skills and knowledge to critically evaluate econometric and applied economics research.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply frontier knowledge to solve complex, real-world problems.
LO2.
Critically interpret and independently evaluate issues to deliver innovative and effective solutions.
LO3.
Conduct themselves in an ethical and socially responsible way by adopting forward-looking solutions to support a more sustainable economy.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Tutorial/ Problem Set | Problem Set 1 | 30% |
31/03/2025 3:59 pm |
| Paper/ Report/ Annotation, Tutorial/ Problem Set | Problem Set 2 | 30% |
12/05/2025 3:59 pm |
| Paper/ Report/ Annotation, Project | Replication Paper | 40% |
9/06/2025 3:59 pm |
Assessment details
Problem Set 1
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 30%
- Due date
31/03/2025 3:59 pm
- Learning outcomes
- L01, L02, L03
Task description
Analytical and data-based problems.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This assessment must be submitted by the due date and time as stated in the course profile. For this course, students are required to submit via Turnitin on the course Blackboard site.
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.
Problem Set 2
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 30%
- Due date
12/05/2025 3:59 pm
- Learning outcomes
- L01, L02, L03
Task description
Analytical and data-based problems.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This assessment must be submitted by the due date and time as stated in the course profile. For this course, students are required to submit via Turnitin on the course Blackboard site.
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.
Replication Paper
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 40%
- Due date
9/06/2025 3:59 pm
- Learning outcomes
- L01, L02, L03
Task description
Replication and extension of an analytical and data-based journal paper. This assignment includes a short video presentation.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This assessment must be submitted by the due date and time as stated in the course profile. For this course, students are required to submit via Turnitin on the course Blackboard site.
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. Course grade description: Failure to indicate any ability to carry out an econometric investigation of even the most elementary kind. No evidence of having mastered the computer software. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Failure to indicate any ability to carry out an econometric investigation of even the most elementary kind. No evidence of having mastered the computer software. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: There is some evidence of being able to construct a sound econometric model and perform the associated estimation; an elementary understanding of the use of computer software. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: There is significant evidence of being able to set up (and describe) an econometric model, estimate it and interpret the results; able to evaluate the adequacy of the estimated model and present the results in writing clearly. Good familiarity with the computer software. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Same as 4 above, in addition able to critically comment on how improvements can be made to an estimated model; demonstrate sound knowledge of all the materials covered in the course. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Same as 5 above, with a demonstration of an excellent knowledge of all the materials and evidence of completing all assignments with good standing. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Same as 6 above, with an almost flawless performance in both examinations and tutorials. |
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 Section 6.1 - Assessment Related Policies & Guidelines. Please refer to 6.1 and the link to theﾠAcademic Integrity Module (AIM). It is strongly recommended that you complete the AIM if you have not already done so.
SUBMISSION OF ASSIGNMENTS:
All assignments must be submitted by the due date and time as stated in the course profile. For this course, students are required to submit all assignments via Turnitin on the course Blackboard site.
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.
Additional learning resources information
This course does not have a required textbook. However, the following textbooks may be useful to support the material introduced during lectures.
Hansen, E.B. (2022), Econometrics, Princeton University Press. (https://www.ssc.wisc.edu/~bhansen/econometrics/)
Greene, W.H. (2008), Econometric Analysis Sixth edition, Pearson/ Prentice Hall.
Hao, L., and D.Q. Naiman (2007), Quantile Regression, Series: Quantitative Applications in the Social Sciences #149. Sage Publications.
Koenker, R. (2005), Quantile Regression, Cambridge University Press.
Cramer, J.S. (1986), Econometric Applications of Maximum Likelihood Methods, Cambridge University Press.
Lancaster, T. (2004), Introduction to Modern Bayesian Econometrics, Basil Blackwell.
Koop, G., Poirier, D.J., and J. Tobias (2007), Bayesian Econometric Methods, Cambridge.
Matyas, L. (1999), Generalized Method of Moments Estimation, Cambridge University Press: New York.
Hall, A. R. (2005), Generalized Method of Moments, Advanced Texts in Econometrics, Oxford University Press.
I. Ahamada and E. Flachaire (2010), Non-Parametric Econometrics, Oxford University Press.
Wooldridge, J.W. (2011), Econometric Analysis of Cross Section and Panel Data Second edition. MIT Press.
Train, K.E. (2003), Discrete Choice Methods with Simulation, 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 (24 Feb - 02 Mar) |
Lecture |
Introduction and Math Review Course introduction; review of elementary probability and statistics; review of basic linear algebra. Learning outcomes: L01, L02, L03 |
Week 2 (03 Mar - 09 Mar) |
Lecture |
Review of Ordinary Least Squares Estimation Review matrix treatment of linear conditional mean modeling; OLS estimator; Gauss-Markov Theorem and assumptions; conditional prediction; loss function. Learning outcomes: L01, L02, L03 |
Week 3 (10 Mar - 16 Mar) |
Lecture |
Regression Modeling and M-Estimation From statistical to econometric models; conditional quantiles (CQ); semiparametric models; M-estimation. Learning outcomes: L01, L02, L03 |
Week 4 (17 Mar - 23 Mar) |
Lecture |
The Likelihood Function and Maximum Likelihood Estimation Basic likelihood concepts; score functions; computation of MLE; asymptotic properties; examples from univariate and regression models. Learning outcomes: L01, L02, L03 |
Week 5 (24 Mar - 30 Mar) |
Lecture |
The Bayesian Framework Quantifying uncertainty about parameters with probabilities defined on random variables; specifying prior distributions; computing posterior distributions from priors updated by the likelihood; examples from univariate and regression models. Learning outcomes: L01, L02, L03 |
Week 6 (31 Mar - 06 Apr) |
Lecture |
Parameter Identification and Instrumental Variable Estimation Simultaneous equations modelling; the Analogy Principle; causal parameters; IV estimation; GMM estimation. Learning outcomes: L01, L02, L03 |
Week 7 (07 Apr - 13 Apr) |
Lecture |
Inference from Data I: Hypothesis Testing Fundamentals of inference based on sampling distributions; key concepts related to hypothesis testing; Wald, criterion-based, and score tests. Learning outcomes: L01, L02, L03 |
Week 8 (14 Apr - 20 Apr) |
Lecture |
Inference from Data II: Posterior Analysis Fundamentals of inference from the posterior distribution; posterior quantiles, highest posterior density intervals; Bayes factors and Bayesian hypothesis testing; connection to sampling distributions, confidence intervals and hypothesis testing. Learning outcomes: L01, L02, L03 |
Week 9 (28 Apr - 04 May) |
Lecture |
Non-parametric and Flexible Parametric Models Kernel density and regression modelling; mixture models; kernel regression; mixture of normals; inference from mixture models; relationship to semiparametric models. Learning outcomes: L01, L02, L03 |
Week 10 (05 May - 11 May) |
Lecture |
Time-series Models Introduction to stochastic processes; forecasting; dynamic effects; key theoretical and empirical challenges when observations are dependent; examples in applied macroeconomics. Learning outcomes: L01, L02, L03 |
Week 11 (12 May - 18 May) |
Lecture |
Panel Models Basics of linear panel models; pooled, random effects and fixed effect models; types of exogeneity and endogenous regressors; GLS and GMM estimation methods; application to MABEL data. Learning outcomes: L01, L02, L03 |
Week 12 (19 May - 25 May) |
Lecture |
High-Dimensional Models and Machine Learning High dimensional regression modelling; Ridge regression; LASSO; penalty variable selection; Bayesian LASSO; double/debiased machine learning. Learning outcomes: L01, L02, L03 |
Week 13 (26 May - 01 Jun) |
Lecture |
Review Revision of key concepts and methods covered throughout the course. 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
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.