Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer
Barry Quinn
Contact via 9850 6475
Room 625, 12WW (E7A)
12pm - 2pm Tuesdays
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Credit points |
Credit points
4
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Prerequisites |
Prerequisites
Admission to MRes
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Corequisites |
Corequisites
STAT710
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Co-badged status |
Co-badged status
STAT822
STAT822 External
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Unit description |
Unit description
This unit is an introduction to the statistical theory and practice of Time Series Analysis. A Time Series is a set of data indexed by time. A time series is modelled as a single 'realisation' or sample of a stochastic process, i.e. a collection of (possibly) dependent random variables. The unit looks at suitable models for time series, examines the estimation of parameters in these models, hypothesis testing (and alternatively estimating the number of parameters), prediction of future values of the time series (forecasting), models for multivariate time series and the estimation of periodicity. There will also be a limited look at modelling stochastic volatility. Emphasis in this unit will be on practice.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Assignments may be handwritten, and submitted in person, or electronically via email to Prof Quinn. There is no requirement that assignments be word-processed, since the mathematical typesetting capabilities of Microsoft word are inadequate.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Assignment 1 | 8% | No | 21st August |
Assignment 2 | 12% | No | 2nd October |
Assignment 3 | 12% | No | 30th October |
Practical Assignment | 18% | No | 6th November |
Final Examination | 50% | No | 10th November |
Due: 21st August
Weighting: 8%
Assignment 1 will be mainly theoretical, but may involve simulation and the writing of Matlab code. It will be available on the iLearn page by 7th August. Submit to the lecturer by 5pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. In the case of the late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
Due: 2nd October
Weighting: 12%
Assignment 2 will be mainly theoretical, but may involve simulation and the writing of Matlab code. It will be available on the iLearn page by 4th September. Submit to the lecturer by 5pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. In the case of the late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
Due: 30th October
Weighting: 12%
Assignment 3 will be mainly theoretical, but may involve simulation and the writing of Matlab code. It will be available on the iLearn page by 9th October. Submit to the lecturer by 5pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. In the case of the late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
Due: 6th November
Weighting: 18%
This will be a practical assignment, with emphasis on time series model fitting, Matlab coding and simulation. It will be posted on iLearn on 9th October. Submit to the lecturer by 5pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. In the case of the late submission of an assignment, if no special consideration has been granted, 10% of the earned mark will be deducted for each day that the assignment is late, up to a maximum of 50%. After 5 days, including weekends and public holidays, a mark of 0% will be awarded for the assignment.
Due: 10th November
Weighting: 50%
The final examination will be a take-home exam, available at 9am local time and finishing at 5pm. Evidence should be given that the examination finished at 5pm. Details will be given closer to the date. There is no “group work” assessment in this unit. All work is to be the student's own. The University's special consideration policy will apply in case you are unable to complete the exam on the day.
https://students.mq.edu.au/study/my-study-program/special-consideration
There are three contact hours per week, usually comprised of two hours of lectures and one hour of practical work. Check the timetable for classes.
Please consult iLearn or the Statistics Department webpage for details of consultation hours.
Technologies used and required
Lecture material will be placed on iLearn. If the lecture room is equipped with Echo facilities, they will be used, i.e. audio and/or video recordings will be available soon after the lecture.
Students will need to use a computer for most of the assessments. There will be extensive use of Matlab during practical classes. Students are entitled to download the full version of Matlab, and use it for the duration of their studies. Details are at
https://web.science.mq.edu.au/it/matlab/
I urge you to install it on your laptop, or home desktop. Please email me if you have difficulty, as the download is rather large.
TOPIC |
MATERIAL COVERED |
1 |
Stationary processes, autocovariances, autocorrelations, the Wold decomposition theorem. |
2 |
Autoregressive moving average (ARMA) processes, the Yule-Walker relations |
3 |
Estimation of ARMA parameters. Goodness of fit. Model building. Estimating the order of ARMA models. |
4 |
Prediction for ARMA processes. |
5 |
Multivariate time series. The Whittle recursion. |
6 |
Stochastic volatility models. |
7 |
Models for periodic phenomena. The estimation of periodicity and applications |
8 | State space models and the Kalman filter. |
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
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Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
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Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
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Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
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This graduate capability is supported by:
There is no prescribed textbook. Some reference books, not in order of relevance, are
The lecture notes are extensive.
Matlab will be used in the practical classes. The full version of Matlab may be installed on students' computers for the duration of their studies. Details are given earlier in this guide. iLab may also be used, as it has a full version of MATLAB.