Course coordinator
Dr Youngsoo Jang
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
This course extends the core materials in the field of macroeconomics beyond that covered in Macroeconomics A (currently ECON6020 Macroeconomic Theory). Whilst Macroeconomics A provides a comprehensive coverage of key, standard macroeconomic analysis at the Honours level, Macroeconomics B is intended to provide a more in-depth, extended coverage of important, contemporary, dynamic macroeconomic topics that cannot be covered in Macroeconomics A due to time constraints. The Macroeconomics B course will be particularly useful for those Honours students who are writing a thesis on a topic related to macroeconomics or who wish to specialise in the area of macroeconomics.
This course focuses on tax and transfer policies using the standard heterogeneous-agent model with uninsurable idiosyncratic income risks.
The course is divided into three parts. In Part 1, we cover an important tool needed to analyze and numerically solve macro models with heterogeneous agents (the Bewly-Hurgett-Ayagari model). In part 1, we study how to solve individual decision rules, aggregate variables, and the factor prices in general equilibrium. Additionally, we learn how to align the model with the data (calibration)
In Part 2, we use the heterogeneous-agent model to study taxes. With the model, we explore optimal proportional income tax, labor income tax, capital income tax, and progressive tax in the long run.ﾠ
In Part 3, we extend the long-run analysis to short- or midterm- analysis with transition dynamics. We study why long-run optimal tax is often hard to achieve in the short run.
Course requirements
Assumed background
Students are expected to have completed ECON6020/7620 Macroeconomics A, or equivalent. ﾠIf you do not have the required background, please contact the course coordinator prior to the beginning of the course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON6020
Incompatible
You can't enrol in this course if you've already completed the following:
ECON7640, ECON8040
Restrictions
BEcon(Hons), BA(Hons)(Economics) and BAdvFin&Econ(Hons)(Economics Field of Study)
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 to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Dr Youngsoo Jang
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
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.
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 27 September. Semester 2 classes recommence Mon 30 September. ﾠ
Aims and outcomes
ECON6040/7640 covers special topics in modern Macroeconomic Theory at the introductory postgraduate level. The aim of the course is to provide students with modelling techniques and knowledge on key topics in macroeconomics at the postgraduate level. It develops coherent, analytical frameworks and uses them to critically evaluate macroeconomic policies.ﾠ
A comprehensive understanding of the macroeconomy, its workings and the ability of economic policy to affect macroeconomic outcomes and economic welfare can only be achieved through theoretical, economic modelling. Macroeconomic models provide academics and both government sector and private sector decision makers with important information and fundamental insights into the workings of the macroeconomy.
The course is an extension of ECON6020/7620. While ECON6020/7620 covers a larger number of general topics in macroeconomics, ECON6040/7640 covers a smaller number of specific topics.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Formulate dynamic problems using recursive methods and optimal control tools.
LO2.
Apply computational methods to solve dynamic optimisation problems.
LO3.
Understand important theoretical building blocks of macroeconomic models with heterogeneous agents.
LO4.
Apply macroeconomic models with heterogeneous agents to optimal policy design.
LO5.
Generate research ideas in the context of macroeconomics with heterogeneous agents.
LO6.
Explain research findings and ideas through oral presentations and/or in written form.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Tutorial/ Problem Set | Assignment 1 | 20% |
20/09/2024 11:00 am |
| Computer Code, Tutorial/ Problem Set | Assignment 2 | 20% |
25/10/2024 11:00 am |
| Presentation | Student Presentations | 15% |
21/10/2024 2:00 pm |
| Examination | Final Exam | 45% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
Assessment details
Assignment 1
- Mode
- Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 20%
- Due date
20/09/2024 11:00 am
- Learning outcomes
- L01, L02, L03
Task description
Students are expected to apply the tools covered in the lectures, including the computational component of the course.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative 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
Submission via Blackboard by the due date and time. In addition, students should email their codes for the computational part to the lecturer.
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.
Assignment 2
- Mode
- Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 20%
- Due date
25/10/2024 11:00 am
- Learning outcomes
- L01, L02, L03
Task description
Students are expected to apply the tools covered in the lectures, including the computational component of the course.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative 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
Submission via Blackboard by the due date and time. In addition, students should email their codes for the computational part to the lecturer.
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.
Student Presentations
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 15%
- Due date
21/10/2024 2:00 pm
- Learning outcomes
- L04, L05, L06
Task description
Students are required to present the contributions and results of specific papers related to the topics covered in class.
The presentation is oral and students should prepare his/her own slides. The duration of the presentation will depend on the number of enrolled students, and an additional make up class might need to be scheduled for some presentations. Further details will be provided in class.
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.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Final Exam
- Mode
- Written
- Category
- Examination
- Weight
- 45%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L02, L03, L04
Task description
The final exam will cover material from all the lectures in the course and will consist of short answer and problem solving questions.
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 | Any calculator permitted |
| 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 link to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIMﾠif you have not already done so.
SUBMISSION OF ASSIGNMENTS
All assignments must be submitted by the due date and time stated in the course profile. For this course, students are required to submit Problem Set/sﾠelectronically via Blackboard. In addition, students should email their codes for the computational part to the lecturerﾠby the due date and time.
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.
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 |
Tools Dynamic optimization, numerical methods (Bisection Method, Golden Section Search, Linear Interpolation) Learning outcomes: L01, L02, L03 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
Incomplete Markets (Part 1) Foundations, Bewley-Aiyagari model: Computing Decision Rules, Aggregates, and Equilibrium. Learning outcomes: L01, L02, L03 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
Incomplete Markets (Part 1) Foundations, Bewley-Aiyagari model: Computing Decision Rules, Aggregates, and Equilibrium. Learning outcomes: L01, L02, L03 |
Week 4 (12 Aug - 18 Aug) |
Lecture |
Incomplete Markets (Part 1) Foundations, Bewley-Aiyagari model: Calibration Learning outcomes: L01, L02, L03 |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Incomplete Markets (Part 2) Foundations, Bewley-Aiyagari model: optimal policy and other applications in the long run Learning outcomes: L01, L02, L03 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
Incomplete Markets (Part 2) Foundations, Bewley-Aiyagari model: optimal policy and other applications in the long run Learning outcomes: L01, L02, L03 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
Incomplete Markets (Part 2) Foundations, Bewley-Aiyagari model: optimal policy and other applications in the long run Learning outcomes: L01, L02, L03 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Incomplete Markets (Part 2) Foundations, Bewley-Aiyagari model, optimal policy and other applications in the long run Learning outcomes: L01, L02, L03 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
Incomplete Markets (Part 3) Foundations, Bewley-Aiyagari model: optimal policy and other applications with transition dynamics Learning outcomes: L01, L02, L03, L04 |
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid Semester Break & Public Holiday No class Learning outcomes: L05, L06 |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Incomplete Markets (Part 3) Foundations, Bewley-Aiyagari model: optimal policy and other applications with transition dynamics Learning outcomes: L01, L02, L03, L04 |
Week 11 (07 Oct - 13 Oct) |
No student involvement (Breaks, information) |
King's Birthday We do not have a class. Learning outcomes: L01, L02, L03, L04 |
Multiple weeks From Week 12 To Week 13 |
Workshop |
Student Presentations Depending on student enrolment numbers, an additional make up session might need to be scheduled for the presentations. Learning outcomes: L01, L02, L03, L04, L05, L06 |
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.