25573 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 2020 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 25503 Investment Analysis OR 25571 Introductory Econometrics OR 23571 Introductory Econometrics
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject equips students with a general knowledge of model building, which stands them in good stead for basic empirical work in business environments. The approach to modelling, and the reasoning about multi-variable empirical relationships, strengthens students' analytic skills. 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 finance.
Subject learning objectives (SLOs)
| 1. | demonstrate the practical skills needed to fit models to time series data |
|---|---|
| 2. | describe the statistical properties of time-series models and explain why they work in certain contexts and not in others |
| 3. | apply various techniques to model, estimate and forecast univariate time series |
| 4. | apply methods to model, estimate and forecast multivariate time series |
| 5. | apply methods to model, estimate and forecast higher order moments of time series |
Contribution to the development of graduate attributes
The subject contributes to Bachelor of Business degree by preparing students to commence a fulfilling and effective career in business, especially as an economist or finance professional. The subject makes its contribution by examining the special statistical characteristics that arise when modelling time series data, such as unemployment rates, inflation rates, commodity prices, interest rates and exchange rate data, that have been collected at a regular frequency (such as daily, weekly, monthly or quarterly intervals). The subject will make use of an advanced econometrics software package. Such skills will be developed through practice-based problems and assessments experienced in lecture and tutorial classes, via practical assessment tasks, online self-assessment tools and final exam. This subject builds upon knowledge developed in economics, corporate finance and investment areas.
This subject contributes to the development of the following UTS Business School graduate attributes:
- critical thinking
- creativity and analytical skills
- business practice oriented skills
Teaching and learning strategies
Time Series Econometrics is taught using a combination of interactive lecture and tutorial classes supplemented with flipped learning activities. UTSOnline 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 UTSOnline.
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 the recommended textbook readings. For specific topics, additional video content and exercises will be available on UTSOnline 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 UTSOnline. Students are encouraged to engage with each other and staff on UTSOnline 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)
- Basic regression analysis with time series data
- Issues in using OLS with time series data
- Serial correlation in time series regressions
- Basic regression analysis with time series data Issues in using OLS with time series data
- Advanced time series topics
- ARIMA models and forecasting
- Vector autoregressive models and forecasting
- Models of second moments
- Empirical projects
Assessment
Assessment task 1: Tutorial Exercises (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3, 4 and 5 |
|---|---|
| Weight: | 30% |
Assessment task 2: Assignment (Group)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 4 |
|---|---|
| Weight: | 20% |
Assessment task 3: Final Exam (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3, 4 and 5 |
|---|---|
| Weight: | 50% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Tsay, R. S. (2013). An Introduction to Analysis of Financial Data with R (third ed.) Wiley.
The subject is based on Chapters 2 and 4 of Tsay (2013), which can be downloaded from the UTS library website.
Recommended texts
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.