General Information
Many types of economic and financial data naturally occur as a series of data points in temporal order. Stock market indices are a classic example of such time series. Standard statistical methods are not appropriate for such data. This subject provides an introduction to time series econometrics with an emphasis on practical applications to typical economic and financial issues. Emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and the use of appropriate software to conduct the analysis.
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Details
Academic unit: Bond Business School Subject code: ECON71-300 Subject title: Advanced Econometrics Subject level: Postgraduate Semester/Year: September 2022 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Seminar: x12 (Total hours: 24) - Seminar 1
- Computer Lab: x12 (Total hours: 24) - Computer Lab 2
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: Books
- Enders, W. (2010). Applied Econometric Time Series. 4th, John Wiley
iLearn@Bond & Email: iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | ECON71-300 |
| Subject title: | Advanced Econometrics |
| Subject level: | Postgraduate |
| Semester/Year: | September 2022 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
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| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
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| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
- Analyse the statistical significance of stationarity of time series through unit root tests
- Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short- and long-run relationships between the variables.
- Demonstrate the advanced knowledge of unit roots and cointegration in the context of panel data regression models.
- Demonstrate the ability to produce a written report that communicates ideas clearly, cogently, and thoroughly, using a professional style and format.
- Demonstrate the ability to work effectively with others to successfully complete a project.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-Aided Examination (Open) Comprehensive final examination. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers. 35% Final Examination Period 1,2,3,4,5,6,7 Computer-Aided Examination (Open) Mid-semester examination. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers. Topics from 1-5. 25% Week 7 (Mid-Semester Examination Period) 1,2,3,7 Project§ Group project modelling multivariate time series data and an extensive forecasting exercise. 20% Week 12 1,2,3,4,5,6,7 Skills Assignment Homework. Use econometrics software to solve prescribed problems and submit professional reports describing your solution. 20% Ongoing 1,2,3,4,5,6,7 - § Indicates group/teamwork-based assessment
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-Aided Examination (Open) | Comprehensive final examination. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers. | 35% | Final Examination Period | 1,2,3,4,5,6,7 |
| Computer-Aided Examination (Open) | Mid-semester examination. Exam format is a combination of statistical and spreadsheet software applications (e.g., Eviews, Excel, R) and written answers. Topics from 1-5. | 25% | Week 7 (Mid-Semester Examination Period) | 1,2,3,7 |
| Project§ | Group project modelling multivariate time series data and an extensive forecasting exercise. | 20% | Week 12 | 1,2,3,4,5,6,7 |
| Skills Assignment | Homework. Use econometrics software to solve prescribed problems and submit professional reports describing your solution. | 20% | Ongoing | 1,2,3,4,5,6,7 |
- § Indicates group/teamwork-based assessment
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
Homework assignment questions will be assigned for each topic.There will be 4 homeworks for submission. Homework assignments must be submitted at the beginning of lab session as indicated in the subject outline below. The best three will count towards your homeworks grade. Homework submissions by email will not be entertained and it will result in zero marks. Students may work on their assignment in a group but should write their assignment independently in their own words. If it is not written independently, it will be considered as plagiarism. Due to the voluntary nature of best three assignments out of total of four homework assignments, late submission will result in zero marks.
Academic Integrity
University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Bond University utilises Originality Reporting software to inform academic integrity.Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
Accessibility and Inclusion Support
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
Additional subject information
A peer-evaluation system will be used in this subject to help determine the individual marks for all group assessments. As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Review of Matrix Algebra and Review of Basic Econometrics
Review of matrix algebra and related statistical properties. Derivations of Eigenvalues and Eigenvectors and their applications. Review of classical linear regression model assumptions and their violations.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
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Univariate Stationary Time Series Models and Forecasting
Definition of white noise and stationary time series models are discussed. The autocorrelation functions and partial autocorrelation functions are derived for various stationary Autoregressive Moving Average (ARMA) models. Forecasting errors based on ARMA models are derived and applied.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
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Seasonal Time Series Models, Non-stationary Univariate Time Series Models
The autocorrelation functions and partial autocorrelation functions are derived for various non-stationary ARMA models. Seasonality is captured through stochastic seasonal Autoregressive Integrated Moving Average (ARIMA) models and deterministic seasonal models. Forecasting errors based on non-stationary ARMA models and seasonal ARIMA models are derived and applied.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
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Unit Root Techniques
Explore the limitations of the central limit theorem Unit root tests to test for stationarity of time series is conducted through ADF, PP and KPSS tests. The empirical critical values are obtained through Monte Carlo Simulation and Response Surface Functions.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
- Analyse the statistical significance of stationarity of time series through unit root tests
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Modelling Volatility
Assumption homoscedastic errors are tested and violations are fixed through time-varying conditional volatility models such as Autoregressive Conditional Heteroscedasticity (ARCH), GARCH, TARCH, GJR and GARCH-in-Mean models.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
- Analyse the statistical significance of stationarity of time series through unit root tests
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Vector Autoregression (VAR)
The evolution of stationary multivariate model is established through simultaneous equations models. Short-run Granger causality, impulse responses and variance decompositions are established through VAR models.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short- and long-run relationships between the variables.
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Cointegration and Error Correction Models
Engle and Granger framework to establish the long-run relationship between the variables is discussed. Both long and short-run Granger causality, impulse responses and variance decompositions are established through Engle and Granger cointegrating models.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short- and long-run relationships between the variables.
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Vector Error Correction Models
Johansen procedure to establish the long-run relationship between the variables is discussed. Both long and short-run Granger causality, impulse responses and variance decompositions are established through Vector Error Correction (VECM) models.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short- and long-run relationships between the variables.
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Panel Unit Root Tests and Panel Vector Autoregressive Model
An extension of time series unit root tests is introduced to panel regression framework. The estimation procedures of Panel VAR are models discussed. The consistent estimates are obtained through Generalised Method of Moments (GMM).
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Demonstrate the advanced knowledge of unit roots and cointegration in the context of panel data regression models.
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Non-linear Time Series Models
An extension of linear univariate and multivariate models is discussed. The non-linear models such as Self Exiting Threshold Autoregressive (SETAR) and Markov Switching models and their applications are also discussed in depth.
SLOs included
- Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Apply linear and non-linear univariate techniques of time series models for business forecasts.
- Demonstrate the advanced knowledge of unit roots and cointegration in the context of panel data regression models.