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 covers numerical methods and computational tools that are relevant in solving and quantifying implications of economic models and estimating econometric models. The course also covers how to write computer codes to implement these methods and tools.
This course covers numerical methods and computational tools that are relevant in solving and quantifying implications of economic models and estimating econometric models. The course also covers how to write computer codes to implement these methods and tools.
Course requirements
Assumed background
This course is intensive in writing program codes. Previous experience in any programming language would be helpful.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON6020
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON6300 or 6380
Restrictions
BA(Hons)(Economics); BEcon(Hons)
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.
Course staff
Lecturer
Dr Satoshi Tanaka
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.ﾠ
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 29 September. Semester 2 classes recommence Mon 30 September. ﾠ
Aims and outcomes
The primary aim of this course is to provide students with a clear understanding and knowledge of the main computational tools and methods used in economics.
ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Apply computational tools and models to economic models.
LO2.
Solve economic models to compute equilibria in static and dynamic economic models.
LO3.
Demonstrate proficiency in the use of programming software such as Matlab and Python.
LO4.
Illustrate the link between theory and computation.
LO5.
Critically select and apply computational algorithms for economic models.
LO6.
Construct a plan of analysis to communicate research questions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Presentation | In-Class Presentation | 10% |
22/07/2024 - 21/10/2024 |
| Computer Code, Paper/ Report/ Annotation | Six Problem Sets (Fortnightly, Programming Intensive) | 60% (Each assignment is worth 10%) |
22/07/2024 - 21/10/2024 |
| Computer Code, Paper/ Report/ Annotation | Take Home Assignment (Programming Intensive) | 30% |
22/10/2024 - 25/10/2024
Due at 4pm |
Assessment details
In-Class Presentation
- Mode
- Activity/ Performance
- Category
- Computer Code, Presentation
- Weight
- 10%
- Due date
22/07/2024 - 21/10/2024
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Student will be asked to present the codes and algorithms of their answers in the regular assignments.
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
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Six Problem Sets (Fortnightly, Programming Intensive)
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 60% (Each assignment is worth 10%)
- Due date
22/07/2024 - 21/10/2024
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
There are 6 fortnightly assignments (problem sets), one for every 2 weeks, from Week 2 to Week 13. The due date for each assignment is Monday in the following week. Assignments must be submitted individually and electronically.
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
Submit answers and computer codes to the Course Coordinator by email.
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.
Take Home Assignment (Programming Intensive)
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 30%
- Due date
22/10/2024 - 25/10/2024
Due at 4pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
- Due to the hands-on nature of the course, the final assessment takes the form of a take-home assignment.
- The scope of the assignment. includes all the topics that are covered throughout the course.
- Group work is strictly prohibited. Your work must be submitted individually and electronically
- Further details about the take home assignment. will be posted to our course BB site.
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
Submit answers and computer codes to the Course Coordinator by email.
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. |
| 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.
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 |
Lecture 1: Intro and Python Basics Python programming basics; Basics of computing and numerical methods Learning outcomes: L03 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
Lecture 2: Root Finding Python programming basics; Basics of computing and numerical methods Learning outcomes: L03, L04 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
Lecture 3: Optimization Nonlinear Equations, Optimization, Computing fixed points Learning outcomes: L03, L04 |
Week 4 (12 Aug - 18 Aug) |
No student involvement (Breaks, information) |
Royal Queensland Show holiday No lecture held on this day due to public holiday. |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Lecture 4: Interpolation Function Approximation, Local approximation methods, Global approximation methods, Multidimensional methods. Learning outcomes: L03, L04 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
Lecture 5: Integration and Simulation Function Approximation, Local approximation methods, Global approximation methods, Multidimensional methods. Learning outcomes: L03, L04 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
Lecture 6: Dynamic Programming I Numerical Integration and Differentiation, Monte Carlo and Quasi-Monte Carlo Methods. Learning outcomes: L01, L02, L03, L04, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Lecture 7: Dynamic Programming II Numerical Integration and Differentiation, Monte Carlo and Quasi-Monte Carlo Methods. Learning outcomes: L01, L02, L03, L04, L05 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
Lecture 8: Projection Methods Methods for Functional Equations, Finite-Difference Methods, Projection Methods, Applications. Learning outcomes: L01, L02, L03, L04, L05 |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Lecture 9: Perturbation Methods Methods for Functional Equations, Finite-Difference Methods, Projection Methods, Applications. Learning outcomes: L01, L02, L03, L04, L05 |
Week 11 (07 Oct - 13 Oct) |
Lecture |
Lecture 10: Heterogenous Agent Model (Aiyagari) Numerical Dynamic Programming, Perfect Foresight Models, Rational Expectations Models. (**This lecture will be on Wednesday, not on Thursday) Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 12 (14 Oct - 20 Oct) |
Lecture |
Lecture 11: Heterogenous Agent Model (Krusell-Smith) Numerical Dynamic Programming, Perfect Foresight Models, Rational Expectations Models. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Week 13 (21 Oct - 27 Oct) |
Lecture |
Lecture 12: Heterogenous Agent New Keynsian Model (HANK) Heterogeneous Agent New Keynesian (HANK) models 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.