Course coordinator
Dr Josh Merfeld
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
Introductory applied econometric course for students with basic economic statistics background. Topics covered include: economic models and role of econometrics, linear regression, general linear model, hypothesis testing, specification testing, dummy variables, simple dynamic models and simple cointegration models. Tutorial problems are solved using a relevant econometrics program.
This is an introductory course in applied econometrics. It reviews and builds on the linear regression model. Some materials overlap withﾠintroductory statistics and mathematics courses but are usually considered to be on a more applied level or more advanced level. See the prerequisite courses above.
The models studied in this course have numerous applications in economics, finance, marketing, management, and related areas, whether in business, government, or academic research. As a result, this is one of the most important, useful, and practical subjects a BEL student can learn at UQ, as it is highly valued when applying for jobs where analytical skills are crucial. Importantly, as with any math-related course, it may require a lot of work, industriousness, and persistence for the student to succeed.
A feature of the course is how examples and exercises are drawn from these different discipline areas to illustrate the usefulness and limitations of certain econometric/statistical techniques.
Hands-on experience in applying these techniques is gained through using R, statistical software packages available in the BEL computer laboratories and commonly used in business and government research involving econometrics/statistics. So, it is worth investing time in learning how to use it well. Students can also freely download R and its IDE, RStudio.
More details about the course are given below. Any details may be subject to changes. Changes, updates, or other announcements will be communicated to students via the online Blackboard facility.
Course requirements
Assumed background
Knowledge of elementary differential calculus, basic linear algebra, and undergraduate level probability and statistics.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1310 or 7300
Incompatible
You can't enrol in this course if you've already completed the following:
ECON2300
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
Lecturer
Dr Josh Merfeld
Tutor
Ms April Deng
Mr Phyo Myat Aung
Ms Xinghao Yao
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: 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.
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
- introduce a range of single-equation econometric models and estimation methods commonly used in empirical economics research work (by businesses, government agencies, consultants, etc.);
- provide an understanding of which models and methods should be used in particular contexts;
- provide experience in using statistical software to conduct empirical econometric analyses; and
- provide the skills necessary to read and understand basic econometric work reported by others to support decision-making in business, public policy, etc.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain key concepts and workhorse models in applied econometrics
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.
Apply proper econometric methods for economic research, policy evaluation, and business analysis
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | Research Project 1 | 15% |
18/09/2025 4:59 pm |
| Project | Research Project 2 | 15% |
16/10/2025 4:59 pm |
| Quiz |
Online Periodic Assessments (Online Quizzes)
|
20% (5% for each quiz) |
All online quizzes are due at 16:59 on the due date. |
| Examination | End-of-semester Exam | 50% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
Assessment details
Research Project 1
- Mode
- Written
- Category
- Project
- Weight
- 15%
- Due date
18/09/2025 4:59 pm
- Learning outcomes
- L01, L02
Task description
Answer all questions using a format similar to the answers to your tutorial questions. When you use R to conduct empirical analysis, you should show your R script(s) and outputs (e.g., screenshots for commands, tables, and figures). Your response should be brief and compact when asked to explain or discuss something. To facilitate tutors' grading work, please clearly label all your answers. You should upload your research report via the Turnitin system before the deadline. Do not hand in a hard copy. You can work on this assignment in groups and discuss how to answer these questions with your group members. However, this is not a group assignment, meaning you must answer all the questions in your own words and submit your report separately. The marking system will check the similarity, and UQ's student integrity and misconduct policies on plagiarism apply.
This assessment covers Lectures 1-6.
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.
We strongly suggest you use the Equation Editor in Microsoft Word and submit the Word document or you use LaTeX/Quarto/Markdown and submit the pdf output. You need to upload your report and appendix (if any, as one and only one file) through the Turnitin portal on the course's Blackboard site.
This course ONLY allows file types that Turnitin can check for similarity. Link of File types checked for similarity: https://web.library.uq.edu.au/library-services/it/learnuq-blackboard-help/learnuq-assessment/turnitin-assignments.
The Turnitin portals will close on time at the deadline, and you should allow yourself enough time to complete your assessment and submit it on time, allowing time for possible technical issues or submission problems. In other words, please consider the risk if you decide to wait until the last minute to submit your assessment. If you submit your assignments after the deadline, a late assignment penalty will apply based on the time stamp in the Turnitin system. You can submit your work more than one time, but only your last submission will be marked.
You need to keep evidence of your successful submission, such as the screenshot and/or the receipt email of your Turnitin submission. If you encounter any problem in your submission process, please contact Student IT Support directly.
Submission guidelines
Submit via Turnitin, Blackboard by the due date/time. Email submissions will NOT be accepted.
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.
Research Project 2
- Mode
- Written
- Category
- Project
- Weight
- 15%
- Due date
16/10/2025 4:59 pm
- Learning outcomes
- L01, L02, L03
Task description
Answer all questions using a format similar to the answers to your tutorial questions. When you use R to conduct empirical analysis, you should show your R script(s) and outputs (e.g., screenshots for commands, tables, and figures). Your response should be brief and compact when asked to explain or discuss something. To facilitate tutors' grading work, please clearly label all your answers. You should upload your research report via the Turnitin system before the deadline. Do not hand in a hard copy. You can work on this assignment in groups and discuss how to answer these questions with your group members. However, this is not a group assignment, meaning you must answer all the questions in your own words and submit your report separately. The marking system will check the similarity, and UQ's student integrity and misconduct policies on plagiarism apply.
This assessment covers Lectures 7-10.
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.
We strongly suggest you use the Equation Editor in Microsoft Word and submit the Word document or you use LaTeX/Quarto/Markdown and submit the pdf output. You need to upload your report and appendix (if any, as one and only one file) through the Turnitin portal on the course's Blackboard site.
This course ONLY allows file types that Turnitin can check for similarity. Link of File types checked for similarity: https://web.library.uq.edu.au/library-services/it/learnuq-blackboard-help/learnuq-assessment/turnitin-assignments.
The Turnitin portals will close on time at the deadline, and you should allow yourself enough time to complete your assessment and submit it on time, allowing time for possible technical issues or submission problems. In other words, please consider the risk if you decide to wait until the last minute to submit your assessment. If you submit your assignments after the deadline, a late assignment penalty will apply based on the time stamp in the Turnitin system. You can submit your work more than one time, but only your last submission will be marked.
You need to keep evidence of your successful submission, such as the screenshot and/or the receipt email of your Turnitin submission. If you encounter any problem in your submission process, please contact Student IT Support directly.
Submission guidelines
Submit via Turnitin, Blackboard by the due date/time. Email submissions will NOT be accepted.
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.
Online Periodic Assessments (Online Quizzes)
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 20% (5% for each quiz)
- Due date
All online quizzes are due at 16:59 on the due date.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
There will be four online quizzes (via Blackboard) throughout the semester, approximately fortnightly. Each consists of multiple-choice questions related to the materials covered in lectures and tutorials.
Quiz 1: Lectures 1-3, 15 August - 21 August
Quiz 2: Lectures 4-6, 5 September - 11 September
Quiz 3: Lectures 7-9, 26 September - 9 October
Quiz 4: Lectures 10-12, 24 October - 30 October
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.
The Online Quizzes will be accessible through Blackboard. The deadline for quizzes is during the working hours of UQ's St Lucia campus. If you encounter any problems while doing quizzes, please get in touch with Student IT Support. All quizzes must be submitted through Blackboard before the due date and time specified (in the Blackboard). You should keep evidence of your successful submission, such as the screenshot and/or the receipt email. Please consider the risk if you wait until the last minute to do the quizzes.
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.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
End-of-semester Exam
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Learning outcomes
- L01, L02, L03, L04, L05
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) 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 | Any calculator permitted |
| Open/closed book | Closed book examination - specified written materials permitted |
| 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 Advice:ﾠ Note that success in a mathematics-related course like this highly depends on regular (e.g., every day) work on the subject. Indeed, in this class, you will see that understanding of the materials covered in any lecture often highly depends on the materials you understood from previous lectures. As a result, waiting for the last moment (e.g., a couple of days) before an assessment (e.g., quiz and assignment) to start studying hard, as some students often do, is likely to be aﾠfailure-proneﾠstrategy. Those of you who will be active and pro-active--digesting the lecture notes soon after the lecture was covered (and, if possible, even before it), digesting the definitions, and working out all the steps in derivations and proofs from covered lectures before coming to next lectures and to tutorials--will improve your chances for success and the likelihood to be among the best in the class!
ASSESSMENT TASKS - ADDITIONAL INFORMATION
Assessment for this course involves three types of tasks: (1)ﾠOnline Quizzes (20%), (2) Research Projects (30%), and (3) End of Semester Examination (50%).
Detail of the research projects is provided below:
RESEARCH PROJECTS
Two structured projects are designed so that you are required to apply the techniques learned in this course to analyze the data sets from the real world. As a part of the project, you are expected to set up an economic and corresponding econometric model, write a proposal with research questions, analyze the data to answer the research questions, and finally provide a short research report. Projects will require a minimum of a good working knowledge of R, and any deficiencies in your familiarity with them will be detrimental to your mark.
SUBMISSION OF ASSIGNMENTS
All assignments must be submitted before the due date and time stated in the course profile. For this course, students must submit electronicﾠcopiesﾠto the course webpage in the Blackboard.
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 |
|---|---|---|
| Stock, J.H. and Watson, M.W. (2019). Introduction to econometrics, 4th edition (global edition), Pearson. | http://pearson.com.au/9781292264455 |
Recommended
| Item | Description | Further Requirement |
|---|---|---|
| Wooldridge, J.M. (2015). Introductory Econometrics: a modern approach, sixth edition, Cengage Learning. | https://www.amazon.com.au/Introductory-Econometrics-Michigan-University-Wooldridge/dp/130527010X/ref=sr_1_1?keywords=Introductory+Econometrics%3A+a+modern+approach&qid=1555244278&s=gateway&sr=8-1 |
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 (28 Jul - 03 Aug) |
Lecture |
Introduction Course overview; Introduction to econometrics; Review of probability and statistics. Learning outcomes: L01 |
Week 2 (04 Aug - 10 Aug) |
Lecture |
Linear Regression with One Regressor Population linear regression model (LRM); Ordinary least squares (OLS) estimator and sample regression line; Measures of fit of the sample regression; Least squares assumptions; Sampling distribution of the OLS estimator. Learning outcomes: L01, L02 |
Week 3 (11 Aug - 17 Aug) |
Lecture |
Linear Regression with One Regressor: Inference Hypothesis tests; Confidence intervals; Regression when X is binary; Heteroskedasticity and homoskedasticity; Efficiency of OLS and the Student t distribution. Royal QLD Show public holiday on Wednesday 13th August. Students allocated to the Wednesday tutorial are invited to attend another tutorial session for this week only. Learning outcomes: L01, L02 |
Week 4 (18 Aug - 24 Aug) |
Lecture |
Linear Regression with Multiple Regressors Omitted variable bias; Causality and regression analysis; Multiple regression and OLS; Measures of fit; Sampling distribution of the OLS estimator. Learning outcomes: L02 |
Week 5 (25 Aug - 31 Aug) |
Lecture |
Inference in Multiple Regressions Hypothesis tests and confidence intervals for one coefficient; Joint hypothesis tests on multiple coefficients; Other types of hypotheses involving multiple coefficients; Control variables; Variable selection. Learning outcomes: L02 |
Week 6 (01 Sep - 07 Sep) |
Lecture |
Nonlinear Regression Functions Strategy for modelling nonlinear functions; Non-linear functions of a single independent variable; Interactions between independent variables; Application. Learning outcomes: L02 |
Week 7 (08 Sep - 14 Sep) |
Lecture |
Regression with Panel Data Panel Data: What and Why; Fixed Effects Regression; Regression with Time Fixed Effects; Standard Errors for Fixed Effects Regression; Application. Learning outcomes: L02, L03 |
Week 8 (15 Sep - 21 Sep) |
Lecture |
Regression with a Binary Dependent Variable Linear probability model; Probit and Logit regression; Estimation and inference in Probit and Logit models; Application. Learning outcomes: L05 |
Week 9 (22 Sep - 28 Sep) |
Lecture |
Instrumental Variables Regression IV Regression; Two-Stage Least Squares; General IV Regression Model; Checking Instrument Validity; Application. Learning outcomes: L02, L04 |
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Semester Break (No Lecture and Tutorials) There will be no lectures, tutorials, or consultation sessions this week. |
Week 10 (06 Oct - 12 Oct) |
Lecture |
Experiments and Quasi-Experiments Potential Outcomes, Causal Effects, and Idealized Experiments; Threats to Validity of Experiments; Quasi-Experiments (Differences-in-Differences, IV Estimation, etc.); Threats to Validity of Quasi-Experiments. King's Birthday public holiday on Monday 6th October. Students allocated to the Monday tutorial and invited to attend another tutorial session for this week only. Learning outcomes: L03 |
Week 11 (13 Oct - 19 Oct) |
Lecture |
Introduction to Time Series Time Series Data; Regression Models for Forecasting; Lags, Differences, Autocorrelation, and Stationarity; Autoregression and ADL Models; Application. Learning outcomes: L02 |
Week 12 (20 Oct - 26 Oct) |
Lecture |
Prediction with Many Regressors and Big Data Big Data; Ridge Regression; Lasso; Principle Components; Application. Learning outcomes: L01, L02, L05 |
Week 13 (27 Oct - 02 Nov) |
Lecture |
Revision Review Lectures 1-12. 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
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.