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.
|Academic unit:||Bond Business School|
|Subject title:||Advanced Econometrics|
Delivery & attendance
|Prescribed resources:|| |
|[email protected] & Email:||[email protected] 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.|
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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.
|Written Report||HW1 - Due in Week 4, Hw2 - Due in Week 6, HW3- Due in Week 9 and HW4 - Due in Week 11.||20%||Ongoing||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%||Mid-Semester Examination Period||1, 2, 3, 4.|
|Project||Project||20%||Week 13||1, 2, 3, 4, 5, 6, 7.|
|Computer-Aided Examination (Open)||Final Examination - in Computer Labs. Writing Answers in Exam Booklet. Software's - Eviews R and Excel||35%||Final Examination Period||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.
|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.|
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.
Students must check the [email protected] 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.
Policy on plagiarism
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.
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.
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.
Matrix operations and manipulations, Linear Regression Models: Assumptions and violations7.
On Conference leave, make-up lecture will be held in week 7