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 2024 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Seminar: x12 (Total hours: 24) - Seminar
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- 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. +++++ BBS uses a self and peer-evaluation system to support students engaged in group-based assessments. Students are expected to provide this feedback in a timely fashion as part of their assessment. The information gathered is used by the educator as partial evidence of equitable contributions by all group members and helps to determine individual marks for group assessments. -
Resources
Prescribed resources: Books
- Enders, W. (2010). Applied Econometric Time Series. 4th, John Wiley
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class 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 2024 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| 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. +++++ BBS uses a self and peer-evaluation system to support students engaged in group-based assessments. Students are expected to provide this feedback in a timely fashion as part of their assessment. The information gathered is used by the educator as partial evidence of equitable contributions by all group members and helps to determine individual marks for group assessments. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class 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: |
Pre-requisites:Co-requisites:There are no co-requisites |
|---|---|
| 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 advanced 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 advanced 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.
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) Exam format is a combination of advanced econometric theory and its application through econometrics packages. 30.00% Week 7 (Mid-Semester Examination Period) 1,2,3 Skills Assignment Use econometrics software to solve prescribed problems and submit professional reports describing your solution. 15.00% Week 5 1,2,3 Skills Assignment Use econometrics software to solve prescribed problems and submit professional reports describing your solution. 15.00% Week 10 4,5 Project Report Forecasting exercise incorporating all advanced econometrics techniques. 40.00% Week 13 1,2,3,4,5 - * 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) | Exam format is a combination of advanced econometric theory and its application through econometrics packages. | 30.00% | Week 7 (Mid-Semester Examination Period) | 1,2,3 |
| Skills Assignment | Use econometrics software to solve prescribed problems and submit professional reports describing your solution. | 15.00% | Week 5 | 1,2,3 |
| Skills Assignment | Use econometrics software to solve prescribed problems and submit professional reports describing your solution. | 15.00% | Week 10 | 4,5 |
| Project Report | Forecasting exercise incorporating all advanced econometrics techniques. | 40.00% | Week 13 | 1,2,3,4,5 |
- * 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
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the Lead Educator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension in writing by the lead educator, a penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
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 advanced 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 advanced 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 advanced 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 advanced 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 advanced 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 advanced mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the advanced 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 advanced mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the advanced 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 advanced mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
- Critically analyse the advanced 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 advanced 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 advanced 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.