- 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
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:
- 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);
- Run and interpret time-series models and regression models for time series
- Use the Box-Jenkins approach to model and forecast time-series data empirically;
- Use multivariate time-series models such as vector autoregression (VAR) to analyse time series data
- Develop fundamental research skills (such as data collection, data processing, and model estimation and interpretation) in applied time series analysis.
- 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
- Typical assessment may include, but is not restricted to: assignments and a final exam. (null) [LO null]
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Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.
Requisite and Incompatibility
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- Student Contribution Band:
- Unit value:
- 6 units
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