Many types of economic and financial data naturally occur as a series of data points in temporal order. Stock market indices are a classic example of such time series. Standard statistical methods are not appropriate for such data. This subject provides an introduction to time series econometrics with an emphasis on practical applications to typical economic and financial issues. Emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and the use of appropriate software to conduct the analysis.
|Faculty||Bond Business School|
1. Demonstrate the mathematical skills needed to derive autocorrelation functions to fit an appropriate univariate time series model.
2. Apply linear and non-linear univariate techniques of time series models for business forecasts.
3. Analyse the statistical significance of stationarity of time series through unit root tests
4. Critically analyse the theoretical and technical knowledge of Vector Autoregressive Models and Vector Error Correction models to establish and differentiate both short- and long-run relationships between the variables.
5. Demonstrate the advanced knowledge of unit roots and cointegration in the context of panel data regression models.
6. Demonstrate the ability to produce a written report that communicates ideas clearly, cogently, and thoroughly, using a professional style and format.
7. Demonstrate the ability to work effectively with others to successfully complete a project.
There are no co-requisites.
|Withdraw – Financial?||10/10/2020|
|Withdraw – Academic?||31/10/2020|
|Withdraw – Financial?||09/10/2021|
|Withdraw – Academic?||30/10/2021|