- Code STAT8002
- Unit Value 6 units
- Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
- ANU College ANU College of Business and Economics
- Course subject Statistics
- Areas of interest Actuarial Studies, Finance, Statistics
- Academic career PGRD
- Dr Hanlin Shang
- Mode of delivery In Person
First Semester 2015
See Future Offerings
This course considers statistical techniques to evaluate processes occurring through time. It introduces students to time series methods and the applications of these methods to different types of data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk management, and sociology). Time series modelling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models will also be considered.
The topics will include: deterministic models; linear time series models, stationary models, homogeneous non-stationary models; the Box-Jenkins approach; intervention models; non-linear models; time-series regression; time-series smoothing; case studies. Statistical software R will be used throughout this course.
Heavy emphasis will be given to fundamental concepts and applied work. Since this is a course on applying time series techniques, different examples will be considered whenever appropriate.
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the requirements for this course, students will be able to:
- LO1: Understand and apply the concept of stationarity to the analysis of time series data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, and sociology);
- LO2: Run and interpret time-series models and regression models for time series
- LO3: Use the Box-Jenkins approach to model and forcast time-series data empirically;
- LO4: Use multivariate time-series models such as vector autoregression (VAR) to analyse time series data
- LO5: Develop fundamental research skills (such as data collection, data processing, and model estimation and interpretation) in applied time series analysis.
- LO6: Use existing R funtion and packages for analysing time series data, and develop their own R code for problem at the end of each chapter in teh textbook as well as additional exercises
See the course outline on the College courses page. Outlines are uploaded as they become available.
- Assignments 1 - 15%
- Assignment 2 - 25%
- Final Examination - 60%
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Students are expected to commit 18 hours/week to completing the work.
Requisite and Incompatibility
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2018||16 Feb 2015||06 Mar 2015||31 Mar 2015||29 May 2015||In Person||N/A|