Course coordinator
Dr Dong-Hyuk Kim
Consultation to be advised.
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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
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 econ2300@uq.edu.au.
Course staff
Lecturer
Dr Dong-Hyuk Kim
Tutor
Ms April Deng
Mr Davis Dingle
Mr Francisco Tavares Garcia
Mr Samuel Buckland
Mr Matthew Chan
Ms Sima Dey
Senior tutor
Mr Dominic Byrne
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Teaching Week 1.
Tutorials commence in Teaching Week 2.
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.
The timetable is published through the UQ Public Timetable found in the APPs section of myUQ.
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
Mid-Semester Break: 23 - 29 September. Semester 2 classes recommence Monday 30 September.
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 | 30% (7 best out of 10) |
Online Periodic Assessments Throughout the Semester |
| Project | Project I: Assignment and Brief Research Report | 15% |
10/09/2024 4:00 pm |
| Project | Project II: Assignment and Brief Research Report | 15% |
25/10/2024 4:00 pm |
| Examination |
Final Exam
|
40% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
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
- Mode
- Written
- Category
- Quiz
- Weight
- 30% (7 best out of 10)
- Due date
Online Periodic Assessments Throughout the Semester
- Learning outcomes
- L01, L02
Task description
Online quizzes (via Blackboard) throughout the semester from Week 2. The exact dates will be announced on Blackboard. There will be a 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.
Marks will be awarded as indicated for each question on the quiz. The highest 7 scores across 10 quizzes will be counted towards a student's final grade for the course. By default, quizzes will be marked automatically by Blackboard; however, students will be given the opportunity to obtain partial marks for incorrect final answers by explaining the steps taken in the derivations and clearly identifying where the error occurred.
This assessment task evaluates students' abilities, skills, and knowledge without the aid of artificial intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online via Blackboard.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
No extensions are possible, even with a medical certificate. CML access is blocked after the due date and time.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
No late submission will be accepted. No extensions are possible, even with a medical certificate. CML access is blocked after the due date and time.
Project I: Assignment and Brief Research Report
- Mode
- Written
- Category
- Project
- Weight
- 15%
- Due date
10/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, 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.
Marks awarded will depend upon the completeness of the project undertaken including the selection of techniques; setting up hypotheses (if relevant); and the quality of the research report submitted.
This assessment task evaluates student’s abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students are required to submit an electronic copy to the Course Coordinator through the course webpage (Blackboard).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 14 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.
Project II: Assignment and Brief Research Report
- Mode
- Written
- Category
- Project
- Weight
- 15%
- Due date
25/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L04, 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.
Marks awarded will depend upon the completeness of the project undertaken including the selection of techniques; setting up hypotheses (if relevant); and the quality of the research report submitted.
This assessment task evaluates student’s abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students are required to submit an electronic copy to the Course Coordinator through the course webpage (Blackboard).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 14 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.
Final Exam
- Hurdle
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This is a closed book exam 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
Marks will be shown on the paper next to each question. Up to half marks will be awarded for incorrect numerical answers, provided correct methods/formulas have been used.
This assessment task provides a verifiable assessment of the students' abilities, skills and knowledge.
Hurdle requirements
Students must obtain a minimum of 48% in the exam to pass the course.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 - no 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
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 Policies & Guidelines. Please refer to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIMﾠif you have not already done so.
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 (22 Jul - 28 Jul) |
Lecture |
Introduction Introduction to econometrics; review of statistical concepts; introduction to R. Learning outcomes: L01, L02 |
Week 2 (29 Jul - 04 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, L02 |
Week 3 (05 Aug - 11 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. Learning outcomes: L01, L02, L03 |
Week 4 (12 Aug - 18 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. NOTE: Wednesday (14/09) is the Royal Queensland Show day (public holiday). There are no tutorials or consultations on a public holiday. Attend sessions scheduled on other days. Learning outcomes: L01, L02, L05 |
Week 5 (19 Aug - 25 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, L05 |
Week 6 (26 Aug - 01 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 (02 Sep - 08 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: L03, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Regression with Panel Data 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. Learning outcomes: L01, L02, L03, L04 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
Regression with a Binary Dependent Variable 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. Learning outcomes: L01, L02, L05 |
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
No lecture or tutorials |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Instrumental Variables Regression 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: L01, L02, L04 |
Week 11 (07 Oct - 13 Oct) |
Lecture |
Experiments and Quasi-Experiments potential outcomes, causal effects, and idealized experiments; threats to validity of experiments; quasi-experiments and potential problems; case study: effect of class size reductions. NOTE: Monday (7/10) is King's Birthday (public holiday). There are no tutorials or consultations on a public holiday. Attend sessions scheduled on other days. Learning outcomes: L02, L03, L04 |
Week 12 (14 Oct - 20 Oct) |
Lecture |
Prediction with Many Regressors and Big Data OLS with many regressors, Ridge regression, Lasso method, Principle Components Analysis. Learning outcomes: L01, L04, L05 |
Week 13 (21 Oct - 27 Oct) |
Lecture |
Review 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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.