23507 Time Series Econometrics
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particular session, location and mode of offering is the authoritative source
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): ((23571 Introductory Econometrics OR 25503 Investment Analysis) AND 26134 Responsible Evidence-Based Decisions)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 25573 Time Series Econometrics
Description
This subject equips students with a general knowledge of building models using empirical time series data. Students learn how to develop, validate and apply models used by business and policy makers to analyse and forecast economic and financial data. Students develop technical and analytical skills through applied practice-based problems. The fundamental knowledge and skills developed in this subject are necessary for a career in economics, finance, and other business disciplines.
Subject learning objectives (SLOs)
| 1. | communicate time series analysis to both academic and professional audiences |
|---|---|
| 2. | use statistical and time series packages in R |
| 3. | manage team projects for complex problems |
| 4. | derive basic mathematics of time series econometrics |
| 5. | identify opportunities and challenges of using time series analytics in a business environment |
Contribution to the development of graduate attributes
This subject builds on the concepts and skills learned in Business Statistics and Introductory Econometrics. The subject continues the development of quantitative skills to help students work with data when having to analyse complex problems. This subject is aligned with the following Graduate Attribute(s):
- Intellectual rigour and innovative problem solving
- Professional and technical competence
Teaching and learning strategies
Time Series Econometrics is taught using a combination of interactive lecture and tutorial classes supplemented with flipped learning activities. Canvas will be used to share information about the subject, to provide in-class and self-study material and to encourage student interaction with staff and other students. The subject outline, lecture slides and supplements, computer lab exercises and solutions, online quizzes and video content are all available on Canvas.
Pre-class activities: Students are expected to complete the following flipped learning activities before attending class throughout the semester. Along with the lecture notes, students are required to regularly read and reflect upon there commended textbook readings. For specific topics, additional video content and exercises will be available on Canvas to supplement student learning. This preparation will allow students to enhance their active learning experiences during lectures. Due to complex nature of computer lab modelling exercises, students should ensure that they have attempted the questions prior to attending class in order to promote their active learning experiences. To assist students with understanding the subject material, additional practice problems (with solutions) are provided. In addition, self-assessment online quizzes which provide immediate feedback on correct and incorrect answers available on Canvas. Students are encouraged to engage with each other and staff on Canvas to support them with their flipped learning activities and subject content.
Class-based activities: Students are expected to attend and participate in all lectures and computer labs. Lectures have a 120 minute duration and computer labs have a 60 minute duration. You are expected have completed the flipped learning activities prior to coming to class. The subject assessment tasks are based on the assumption that students attend all classes and are active in the learning process. Regular attendance at lectures and labs enhance active and collaborative learning experiences via: keeping up to date with the topics covered; filling gaps in the understanding of subject material through personal contact with teaching staff; obtaining staff and peer feedback; and completing practice-based modelling exercises. The lecture time will be spent presenting the technical content of new econometric methods and applying these in actual modelling exercises. Students will be asked to comment on the empirical examples. The lecture’s focus allows students to develop low-level understanding and knowledge of ideas. Subsequently students will be asked to prepare solutions to exercises in advance of a computer lab session. The act of preparing these solutions is designed to allow students to develop higher-level learning, including the application of econometric tools, analysis and synthesis. The Lab sessions will start with a solution to the assigned modelling question and an opportunity for students to question any aspect of the solution and resolve any queries. Next, the students will be given a new problem (generally an extension to the modelling exercise) and then asked to work on a solution in small groups (3-4 students). In order to facilitate feedback, the groups will be expected to present their thinking and arguments in written form. A general class discussion will complete the exercise. The lab sessions aim is to deepen student’s understanding and, through active participation, develop generic skills.
Feedback: There are several avenues for feedback on your learning. Computer lab questions, recommended textbook questions, online self-assessment quizzes, the authentic practice-based modelling assignments, and the final exam offer a multitude of ways for a student demonstrate their level of understanding of the subject content. The online self-assessment quizzes, and computer lab and assigned textbook questions provide regular feedback on your understanding of the subject matter. Written feedback on the authentic practice-based group and individual modelling exercises will be provided in a timely manner. Students also have the opportunity of individual feedback on their learning during the weekly consultation hours.
Content (topics)
- Time series visualisation
- Summary statistics of time series
- Components of time series
- Introductory linear algebra
- AR, MA, ARMA, and ARIMA models
- Regression models with autocorrelated errors
- ARDL models and Granger causality
- ARCH and GARCH models
- Vector autoregressive models
- Impulse response functions
- State space models
- Programming in R
Assessment
Assessment task 1: In-Class Data Analysis (Group)*
| Objective(s): | This addresses subject learning objective(s): 1, 3, 4 and 5 |
|---|---|
| Weight: | 30% |
| Length: | Data projects are meant to be small, but depend on lecture content. Usually, the final report should be succinct and less than 4 pages, including tables and figures. |
| Criteria: |
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 2: Review Report (Individual)
| Objective(s): | This addresses subject learning objective(s): 2, 4 and 5 |
|---|---|
| Weight: | 20% |
| Length: | 1200 words |
| Criteria: |
To demonstrate critical thinking, students will summarise the aims and significance of the reviewed paper, evaluate if the paper has closed the claimed gap in the literature, and judge the appropriateness of the models for the given research question. Mathematical arguments will be used to justify the use of asymptotic inference, including hypothesis testing and associated conclusions. Students will discuss whether or not the conditions for certain tests are met and what alternatives can be used. Furthermore, students must cite relevant literature to support their judgement and suggest future research. Lastly, the review report must be clearly written in an academic language, following the examples discussed in Lecture 6 and 12. |
Assessment task 3: Final Exam (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 4 and 5 |
|---|---|
| Weight: | 50% |
| Length: | 2-hour exam |
| Criteria: |
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Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Lecture slides and selected sections from:
Tsay, R. S. (2013). An Introduction to Analysis of Financial Data with R (third ed.). Wiley.
Lutkepohl, H. and Kratzig, M. (2004). Applied Time Series Econometrics. Cambridge University Press.
Kitagawa, G. (2010). Introduction to Time Series Modeling (first ed.). Chapman and Hall/CRC.
All three books can be accessed online via UTS library.
Recommended texts
Shumway R.H., Stoffer, D.S. (2012). Time Series Analysis and Its Applications With R Examples, 4th Edition, Springer. ISBN-978-3-319-52451-1
Brooks, C. (2019). Introductory Econometrics for Finance (fourth ed.). Cambridge University Press.
Stock, J. H. and M. W. Watson (2020). Introduction to Econometrics (fourth ed.). Pearson.
Other resources
Lecture slides will be posted on UTSOnline.