Course overview
The aim of this course is to study time series methods in econometrics. Students are expected to have knowledge in statistics and Level IV econometrics or equivalent. Topics typically include stationarity, unit roots, autoregressive moving average (ARMA), forecasting, maximum likelihood estimation (MLE), vector autoregression (VAR), structural vector autoregression (SVAR), and co-integration. The emphasis is on understanding the methods and applying them to real-world data.
Course learning outcomes
- Use various advanced time series econometric methods, estimation methods and related econometric theories.
- Apply these methods to empirical data or develop new time series econometric theories.
- Use a number of specialist software such as Matlab, Gauss, C++, Stata and Eviews.
- Interpret time series models' estimates and analyze the results.
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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.