General Information
This subject provides an introduction to modern time series econometrics with emphasis on practical aspects of time series analysis. The main objectives are to give students a background that will enable them to understand and critically appraise applied work on a economic and financial issues, and to provide students with some practical experience in working with economics /financial data. The emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and how to use EVIEWS and R to carry out the analysis.
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Details
Academic unit: Bond Business School Subject code: ECON13-300 Subject title: Advanced Econometrics Subject level: Undergraduate Semester/Year: September 2017 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Computer Lab: x12 (Total hours: 24) - Laboratory
- Seminar: x12 (Total hours: 24) - Seminar 1
- Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
Attendance and learning activities: -
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: | ECON13-300 |
| Subject title: | Advanced Econometrics |
| Subject level: | Undergraduate |
| Semester/Year: | September 2017 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: |
| 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:
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Appreciate the Nobel piece by Engle and Granger in the context of determining the long-run relationships between the variables.
- Extend the Engle and Granger methodology to multivariate context with the notion of vector error correction models.
- Update students with recent time series econometrics models such as GARCH, panel unit root and panel co-integration models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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) Final Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews R and Excel 35.00% Final Examination Period 1,2,3,4,5,6,7 Computer-Aided Examination (Open) Mid-semester Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews, R and Excel. Week 7 - During Lab Session 25.00% Mid-Semester Examination Period 1,2,3,4 Project Project 20.00% Week 13 1,2,3,4,5,6,7 Written Report HW1 - Due in Week 4, Hw2 - Due in Week 6, HW3- Due in Week 9 and HW4 - Due in Week 11. 20.00% Ongoing 1,2,3,4,5,6,7 - * 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) | Final Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews R and Excel | 35.00% | Final Examination Period | 1,2,3,4,5,6,7 |
| Computer-Aided Examination (Open) | Mid-semester Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews, R and Excel. Week 7 - During Lab Session | 25.00% | Mid-Semester Examination Period | 1,2,3,4 |
| Project | Project | 20.00% | Week 13 | 1,2,3,4,5,6,7 |
| Written Report | HW1 - Due in Week 4, Hw2 - Due in Week 6, HW3- Due in Week 9 and HW4 - Due in Week 11. | 20.00% | Ongoing | 1,2,3,4,5,6,7 |
- * 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
Subject curriculum
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Week 1: Lecture 1 and Lab1: Review of Matrix Algebra and Review of Basic Econometrics
Matrix operations and manipulations, Linear Regression Models: Assumptions and violations.
SLOs included
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 2: No Lecture and no lab session
On Conference leave, make-up lecture will be held in week 7.
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Week 3: Lecture 2: Univariate Stationary Time Series Models and Forecasting
AR, MA and ARMA models and forecasting.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 4: Lecture 3: Seasonal Time Series Models, Non-stationary Univariate Time Series Models
Seasonal Models, ARIMA models.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 5: Lecture 4: Unit Root Techniques
ADF, PP and KPSS Tests.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 6: Lecture 5: Modelling Volatility
Modelling Conditional Heteroskedasticity: ARCH, GARCH, TARCH, EGARCH, GJR, ARCH-M and IGARCH.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Update students with recent time series econometrics models such as GARCH, panel unit root and panel co-integration models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 7: Lecture 6: Vector Autoregression
Estimation, Granger Causality, Impulse Responses and Variance Decompositions.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 8 : Lecture 7: Cointegration and Error Correction Models
Engle and Granger Methodology.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Appreciate the Nobel piece by Engle and Granger in the context of determining the long-run relationships between the variables.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 9: Lecture 8: Vector Error Correction Models
Johansen Methodology.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Extend the Engle and Granger methodology to multivariate context with the notion of vector error correction models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 10: Panel Data Models: Panel Unit Root Tests and Panel Vector Autoregressive Model
IPS Panel Unit Root Test, Panel VAR, Panel VECM.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Extend the Engle and Granger methodology to multivariate context with the notion of vector error correction models.
- Update students with recent time series econometrics models such as GARCH, panel unit root and panel co-integration models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 11: Lecture 10: Non-linear Time Series Models
Regime Switching Models and SETAR Models.
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Update students with recent time series econometrics models such as GARCH, panel unit root and panel co-integration models.
- Use Bloomberg to collect and analyze the economics/ finance data.
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Week 12: Presentations
n/a
SLOs included
- Study the stationary univariate time series models in the context of forecasting. Apply various techniques of time series models, including the seasonal autoregressive moving average (SARIMA) models, regression with ARMA models.
- Study vector autoregressive models as an extension of simultaneous equation models.
- Study the use of unit root testing literature in the context of non-stationary univariate time series models.
- Appreciate the Nobel piece by Engle and Granger in the context of determining the long-run relationships between the variables.
- Extend the Engle and Granger methodology to multivariate context with the notion of vector error correction models.
- Update students with recent time series econometrics models such as GARCH, panel unit root and panel co-integration models.
- Use Bloomberg to collect and analyze the economics/ finance data.