Course coordinator
Professor Rodney Strachan
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
Econometric tools that apply to financial and macroeconomics data. Core content includes: characteristics of time series data; capital asset pricing models; co-integrated models; volatility models; models of price changes. Applications include models of stock prices, derivatives, exchange rates, interest rates.
This course is best viewed as a stepping stone between introductory econometric methods and either advanced (e.g. graduate level) econometric studies or direct application of econometric methods in practice. This is a massive gap to fill; hence, the course in necessarily multi-faceted and rich in content.
Lectures introduce concepts and techniques related to stochastic processes as a framework for obtaining inference from economic and financial data. Specific models of stochastic processes and the techniques required to make predictions as well as to investigate dynamic relationships in economics and finance are presented and analysed. Mathematical tools such as polynomial methods are extensively used for this purpose. Labs are focused on applying the techniques in practice using the econometric package, R.
Core content includes: stochastic processes; models of macroeconomic and financial processes; trends, cycles and cointegration models; conditional heteroscedasticity and volatility models; multiple equation models.
Course requirements
Assumed background
Working knowledge of statistical inference, experience implementing regression models, comfortable with learning intermediate-advanced mathematical concepts, basic macroeconomic and finance concepts.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(ECON7310 + (7020 or 7021)) or (ECON7002 + 7310 + (BSFN7401 or COMM7501 or FINM7401)); or (ECON2020 + 2300) or (ECON1020 + 2300 + (BSFN2401 or COMM2501 or FINM2401))
Incompatible
You can't enrol in this course if you've already completed the following:
ECON3300 or 3350
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
Professor Rodney Strachan
Tutor
Han Wang
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 1: Setting up and getting started with RStudio (if you have not already)
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
· Public Holidays: Fri 18 April (Good Friday), Mon 5ﾠMay (Labour Day).
· Mid-Semester Break: 21ﾠApril - 25ﾠApril. Semester 1 classes recommence on Mon 28ﾠApril.
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
This course is tailored to students pursuing greater expertise in modelling and analysing macroeconomic and financial processes. The aim of the course is enable students to apply sophisticated techniques in practical settings with data assumed to be generated by such processes. It will introduce a statistical methodology, including modelling and inferential techniques, that are practical and effective in common applications such as forecasting and inferring dynamic relationships. The ability to combine relevant economic theory, probability theory, modeling methods and empirical techniques using statistical software represents a unique skill set that will set apart sucessful graduates of the course in both future academic environments and industry settings. ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the principles of univariate stochastic processes.
LO2.
Estimate and forecast with univariate time series models.
LO3.
Explain how the modelling of equilibrium relations and the concept of co-integration relate to each other.
LO4.
Apply common volatility models to obtain inference from data generated by financial processes.
LO5.
Assemble and estimate a multivariate time series model.
LO6.
Explain the role of endogeneity and non-linearity in macroeconomic and financial models
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation | Research Report 1 | 20% |
11/04/2025 1:00 pm |
| Paper/ Report/ Annotation | Research Report 2 | 30% |
9/05/2025 1:00 pm |
| Examination |
Final Exam
|
50% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
Assessment details
Research Report 1
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
11/04/2025 1:00 pm
- Learning outcomes
- L01, L02
Task description
The report is a research-oriented task involving real-world data. You will be given a data set and asked to provide policy guidance based on the inference obtained using the empirical tools learned in the course. This is designed to be an authentic assessment that better reflects the skills needed to apply the methods and techniques taught in class. As such, there will be minimal guidance provided; instead, students will have the freedom to make their own decisions in overcoming real-world challenges.
Topics covered:
- forecasting univariate processes I and II.
- trends and cycles
Further details about the report will be provided on our course website.
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 course website.
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.
Research Report 2
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
9/05/2025 1:00 pm
- Learning outcomes
- L01, L02, L03, L06
Task description
The second report is a continuation of the research-oriented tasks involving real-world data and is similar in design to Research Report 1. Again, students should expect minimal guidance to be provided; instead, students will have the freedom to make their own decisions in overcoming real-world challenges.
Topics covered:
- dynamic relationships;
- models of volatility;
- cointegration I and II.
Further details about the report will be provided on our course website.
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 course website.
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.
Final Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Comprehensive closed book final examination during the examination period.
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 or typed notes, double sided, is 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. Course grade description: Failure to indicate any ability to carry out an econometric investigation of even the most elementary kind. No evidence of having mastered the computer software. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. Course grade description: Failure to indicate any ability to carry out an econometric investigation of even the most elementary kind. No evidence of having mastered the computer software. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: There is some evidence of being able to construct a sound econometric model and perform the associated estimation; an elementary understanding of the use of computer software. |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: There is significant evidence of being able to set up (and describe) an econometric model, estimate it and interpret the results; able to evaluate the adequacy of the estimated model and present the results in writing clearly. Good familiarity with the computer software. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Same as 4 above, in addition able to critically comment on how improvements can be made to an estimated model; demonstrate sound knowledge of all the materials covered in the course. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Same as 5 above, with a demonstration of an excellent knowledge of all the materials and evidence of completing all assignments with good standing. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Same as 6 above, with an almost flawless performance in both examinations and tutorials. |
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 and Procedures. Please refer to the Academic Integrity Modules (AIM). It is strongly recommended that you complete the AIM if you have not already done so.
SUBMISSION OF ASSIGNMENTS:
All assignments must be submitted by the due date and time as stated in the course profile. For this course, students are required to submit the projects and final report via Turnitin on the course Blackboard site.
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
Recommended Software
This course does not have a required textbook. Instead, video recordings will be available on Blackboard.
The course uses R for computation exercises. R is a free software for data analysis. Students are required to install R (www.r-project.org/) on their personal computers and recommended to use a free version of RStudio (https://rstudio.com/products/rstudio/download/). Moreover, BEL Computer Labs have R available for students 24/7.
There are many useful online resources. For example:
- A guide for beginners (https://bookdown.org/ndphillips/YaRrr/)
- A lecture note (https://www.econometrics-with-r.org/)
Recommended Resources
Enders, Walter, Applied Econometric Time Series, 4th Edition, 2015. Wiley (HB139 .E55 2015)
Please follow the URL icon on the right to go to the student companion site
An E-textbook version of the book is available (ISBN: 978-1-118-91862-3). Click here
http://www.wiley.com/en-us/Applied+Econometric+Time+Series%2C+4th+Edition-p-9781118918623
Verbeek, M.J.C.M. A Guide to Modern Econometrics, 3rd edition. 2008. Chichester: John Wiley and Sons.(HB139 .V465 2008)
http://eu.he.wiley.com/WileyCDA/HigherEdTitle/productCd-0470517697.html
Patterson, K. An Introduction to Applied Econometrics. A Time Series Approach. 2000. MacMillan Press Ltd. ( HB139 .P373 2000)
Favero, Carlo A., Applied Macroeconometrics, 2001, Oxford University Press. (HB139 .F38 2001)
http://www.igier.unibocconi.it/whos.php?vedi=1071&tbn=albero&id_doc=177
Brooks, C. Introductory econometrics for finance, 2nd ed. 2008. Cambridge University Press (HG173 .B76 2008)
Campbell, John Y., Andrew W. Lo and A. Craig MacKinlay, The Econometrics of Financial Markets, Princeton University Press, 1997. (HG4523 .C27 1997)
Mills, Terence C..The econometric modelling of financial time series. Cambridge, UK ; New York : Cambridge University Press, 2008. (HG174 .M55 2008)
Tsay, Ruey S., Analysis of financial time series. Hoboken, NJ : Wiley, 2010. (HA30.3)
Cuthbertson, Keith, Quantitative Financial Economics. Chichester, England ; Hoboken, NJ : Wiley, c2004. (HG4515.2 .C87 2004)
McKenzie, M.D. and Brooks, R.D., Research Design Issues in Time-Series Modelling of Financial Market Volatility, McGraw-Hill Series in Advanced Finance, Volume 2, 1999. (HG4515.3 .M33 1999)
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 (24 Feb - 02 Mar) |
Lecture |
Lecture 1: Introduction Introduction to the course and revision of basic concepts. |
Week 2 (03 Mar - 09 Mar) |
Lecture |
Lecture 2: Forecasting Univariate Processes - I Introduction to stochastic processes and univariate models. Learning outcomes: L01 |
Week 3 (10 Mar - 16 Mar) |
Lecture |
Lecture 3: Forecasting Univariate Processes - II Implementation of univariate models in practice to obtain predictive inference using a specific data set. Learning outcomes: L01, L02 |
Week 4 (17 Mar - 23 Mar) |
Lecture |
Lecture 4: Trends and Cycles Trend-cycle decomposition of economic processes: explicit modelling and inference. Learning outcomes: L01, L02 |
Week 5 (24 Mar - 30 Mar) |
Lecture |
Lecture 5: Modelling Volatility - I Modelling processes with heteroscedasticity errors using latent volatility: obtaining predictive inference on future volatility. Learning outcomes: L02, L04, L06 |
Week 6 (31 Mar - 06 Apr) |
Lecture |
Lecture 6: Modelling Volatility - II Features of financial processes: explicit modelling and inference. Extensions to stochastic volatility and realised volatility. Learning outcomes: L02, L04, L06 |
Week 7 (07 Apr - 13 Apr) |
Lecture |
Lecture 7: Dynamic Relationships Inference on dynamic relationships among several processes using single equation models. Learning outcomes: L01, L02 |
Week 8 (14 Apr - 20 Apr) |
Lecture |
Lecture 8: Cointegration - I Equilibrium relations, implications of cointegration and spurious regressions. Explicit modelling and inference on cointegrating relations. Friday, April 18th is the Good Friday Public Holiday. If your tutorial is scheduled for this day, please attend any of the other tutorials scheduled for the week. Learning outcomes: L03 |
Mid-sem break (21 Apr - 27 Apr) |
No student involvement (Breaks, information) |
Mid-Semester Break |
Week 9 (28 Apr - 04 May) |
Lecture |
Lecture 9: Cointegration - II Obtaining inference on equilibrium relations and common stochastic trends: identification and estimation. Learning outcomes: L03 |
Week 10 (05 May - 11 May) |
No student involvement (Breaks, information) |
Labour Day Monday, May 5th is the Labour Day public holiday. Therefore, no lecture. Students with Monday tutorials are advised to attend any other tutorials scheduled for the week. |
Week 11 (12 May - 18 May) |
Lecture |
Lecture 10: Multivariate Processes - I Multivariate stochastic processes: modeling using a system of equations and obtaining predictive inference. Learning outcomes: L03, L05, L06 |
Week 12 (19 May - 25 May) |
Lecture |
Lecture 11: Multivariate Processes - II Dynamic relationships in multivariate processes: structural inference on impulse functions, forecast error variance decompositions and Granger non-causality. Learning outcomes: L03, L05, L06 |
Week 13 (26 May - 01 Jun) |
Lecture |
Lecture 13: Revision 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.