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.