Lecturer
Kieran Gibson
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- 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 with single and multiple regressors, hypothesis testing and confidence intervals, dummy variables and nonlinear regression functions, internal and external validity of regression models, panel data models, binary response models, instrumental variable regressions, experiments and quasi-experiments, as well as basic time series analysis. Practical problems are solved using the R econometrics software.
This is an introductory course in applied econometrics. It reviews and builds on the simple linear regression model taught in introductory statistics courses such as ECON1310 and ECON1320. The models studied in this course have numerous applications in economics, finance, marketing, management and related areas.ﾠA feature of the course is the way 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 the use of R, an econometric computer software package available in the BEL computer laboratories. Note also that students can freely download R at [https://www.r-project.org].
Course requirements
Assumed background
In addition to the content of pre-requisite courses, knowledge of elementary differential calculus and basic linear algebra is also assumed.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1310; (For BInfTech students ECON1010 + STAT2004)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON1010, ECON1020 + ECON1050
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 ECON2300@uq.edu.au.
Course staff
Course coordinator
Professor Valentin Zelenyuk
Tutor
Ms April Deng
Mr Davis Dingle
Mr Dominic Byrne
Mr Francisco Tavares Garcia
Mr Clancy McMahon
Mr Eugene Clark
Miss Si Si Gao
Mr Tom Gargan
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), Mon 6 October (King's Birthday).
- Mid-Semester Break: 29 September - 3 October. Semester 2 classes recommence on Tues October 7.
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 R 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.
Apply the concept of linear regression to data and interpret results.
LO2.
Critically evaluate underlying theories, concepts, assumptions and arguments in econometrics.
LO3.
Conduct inference using OLS with one variable and multiple variables in analysing real-world data.
LO4.
Extend the linear regression framework to FE estimators, instrumental variables, and nonlinear regression analysis in the estimation of econometric models.
LO5.
Communicate (with potential users) econometric analysis by tables and figures with proper interpretation and policy recommendations.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Problem Solving, Data Analysis and Short Report
|
25% 7 best out of 10 |
Weeks 3,4,5,6,7,8,9,10,11,12
Online Periodic Assessments Throughout the Semester Due Thursday 4pm in the specified weeks |
| Project |
Project: Assignment and Brief Research Report
|
25% |
26/09/2025 4:00 pm
The project can be submitted at anytime before the due date. |
| Examination |
End-of-semester Exam
|
50% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Problem Solving, Data Analysis and Short Report
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 25% 7 best out of 10
- Due date
Weeks 3,4,5,6,7,8,9,10,11,12
Online Periodic Assessments Throughout the Semester
Due Thursday 4pm in the specified weeks
- Learning outcomes
- L01, L03
Task description
Online quizzes (via Blackboard) throughout the semester from Week 3, exact dates will be announced on Blackboard. There will be in total of 10 quizzes.
1) Five will consist of multiple-choice and short-answer questions related to the material covered in lectures and tutorials.
2) Five will consist of R exercises related to the material covered in lectures and tutorials.
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
Online via Blackboard. No late submission will be accepted.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Project: Assignment and Brief Research Report
- Online
- Mode
- Written
- Category
- Project
- Weight
- 25%
- Due date
26/09/2025 4:00 pm
The project can be submitted at anytime before the due date.
- Other conditions
- Student specific.
- Learning outcomes
- L01, L03, L05
Task description
You are expected to formulate hypothesis relevant to a research project. Analyse the data using techniques covered under the Learning Objectives as indicated above. Submit a research report summarising your findings and offering policy advice based on your findings.
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
Students are required to submit an electronic copy through the course webpage (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.
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
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This is a closed book exam (one A4 single sided sheet of handwritten notes permitted) to be sat in a designated examination room at the St Lucia campus. The exam is comprehensive. However, emphasis will be on:
- Regression with Panel Data
- Regression with Binary Dependent Variable
- Instrumental Variables Regression
- Experiments and Quasi-Experiments
- Big Data
Please note UQ's policy on approved calculators that can be brought into examination rooms: https://my.uq.edu.au/services/manage-my-program/exams-and-assessment/sitting-exam/approved-calculators
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.
Hurdle requirements
- You must achieve at least 45% on this item to pass the course. If your mark is less than 45% of the total marks available on the End-of-semester Exam, the maximum possible grade you can receive is a grade of 3. - Marks will be shown on the paper next to each question. Partial marks will be awarded for incorrect numerical answers, provided correct methods/formulas have been used.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes , single sided, is permitted Non-electronic bilingual dictionary |
| 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.
SUBMISSION OF QUIZZES
Online quizzes will be accessible through Blackboard at the beginning of the week that they are due to be submitted. All quizzes must be submitted through Blackboard by the due date and time specified. No late submissions will be accepted.
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
Wooldridge, J.M. (2006) Introductory Econometrics: A Modern Approach, 5thﾠed.ﾠ Mason Oh.ﾠ South-Western.ﾠ HB139 .W63 2013.
Ramanathan, R. (2002) Introductory Econometrics with Applications, 5th ed. ﾠMason Oh.ﾠ South-Western.ﾠ HB139 .R337 2002.
Kennedy, P.ﾠ(2003)ﾠA Guide to Econometrics, 5th ed.ﾠ Cambridge, Mass.ﾠ MIT Press.ﾠ HB139 .K45 2003.
Studenmund, A.H.ﾠ(2006) Using Econometrics, A Practical Guide, 5th ed.ﾠ Boston, Mass.ﾠ Pearson Higher Education/Addison Wesley. HB139 .S795 2006.
Gujarati, D. (2003) Basic Econometrics.ﾠBoston. ﾠMcGraw-Hill.ﾠ HB139 .G84 2003.
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 Introduction to econometrics; review of statistical concepts; introduction to R. Learning outcomes: L02 |
Week 2 (04 Aug - 10 Aug) |
Lecture |
Linear Regression with One Regressor the linear regression model; estimating the coefficients; measure of fit; the least squares assumptions; sampling distribution of the OLS estimator. Learning outcomes: L01 |
Week 3 (11 Aug - 17 Aug) |
Lecture |
Single Regressor Models: Inference testing hypotheses about one of the regression coefficients; confidence intervals for a regression coefficient; dummy variable regressors; heteroskedasticity and homoskedasticity; foundations of OLS; t-statistics and sample size. Royal QLD Show on Wednesday 13th August. Students allocated to the the Wednesday tutorial are invited to attend any other tutorial session for this week only. Learning outcomes: L02, L03 |
Week 4 (18 Aug - 24 Aug) |
Lecture |
Linear Regression with Multiple Regressors omitted variable bias; the multiple regression model; the OLS estimator in multiple regression; measure of fit in multiple regression; the least squares assumption in multiple regression; the distribution of the OLS estimator in the multiple regression; multicollinearity. Learning outcomes: L01, L02, L03 |
Week 5 (25 Aug - 31 Aug) |
Lecture |
Multiple Regression: Inference hypothesis tests and confidence intervals for single coefficient; tests of joint hypotheses; testing single restrictions involving multiple coefficients; model specification for multiple regression; case study: test score data. Learning outcomes: L02, L03 |
Week 6 (01 Sep - 07 Sep) |
Lecture |
Nonlinear Regression Functions a general strategy for modeling nonlinear regression functions; nonlinear functions of a single independent variable; interactions between independent variables; case study: nonlinear effects on test scores. Learning outcomes: L01, L02, L03 |
Week 7 (08 Sep - 14 Sep) |
Lecture |
Assessing Studies Based on Multiple Regression internal and external validity; threats to internal validity of multiple regression analysis; internal and external validity when the regression is used for forecasting; case study: test scores and class size. Learning outcomes: L01, L02, L03, L05 |
Week 8 (15 Sep - 21 Sep) |
Lecture |
Regression with Panel Data Sub-activity: Quiz 6 --- Due on Thursday, 4 PM Learning outcomes: L03, L04, L05 |
Week 9 (22 Sep - 28 Sep) |
Lecture |
Regression with a Binary Dependent Variable description and examples of panel data; panel data with two time periods; fixed effects regression; regression with time fixed effects; the fixed effects regression assumptions and standard errors for fixed effects regression; case study: drunk driving laws and traffic deaths. Sub-activity: Project Due on Friday 26 September, 2025 at 4PM Learning outcomes: L03, L04, L05 |
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Semester Break |
Week 10 (06 Oct - 12 Oct) |
Lecture |
Instrumental Variables Regression binary dependent variable and the linear probability model; probit and logit regression; estimation and inference in the probit and logit models; case study: Boston HDMA data. King's Birthday public holiday on Monday 6th October. Students allocated to the Monday tutorial are invited to attend another tutorial session for this week only. Learning outcomes: L03, L04, L05 |
Week 11 (13 Oct - 19 Oct) |
Lecture |
Experiments and Quasi-Experiments the IV estimator with a single regressor and a single instrument; the general IV regression model; checking instrument validity; finding valid instruments; case study: demand for cigarettes. Learning outcomes: L03, L04, L05 |
Week 12 (20 Oct - 26 Oct) |
Lecture |
Big Data potential outcomes, causal effects, and idealized experiments; threats to validity of experiments; quasi-experiments and potential problems; case study: effect of class size reductions. Learning outcomes: L03, L04, L05 |
Week 13 (27 Oct - 02 Nov) |
Lecture |
Revision Learning outcomes: L04 |
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.