Econometrics is a sub-discipline of both statistics and economics and presents one interface between statistical theory and the real world. It provides the tools with which to test hypotheses and to generate forecasts of business activity. Topics include the classical regression model, remedial measures for violation of regression assumptions, binary choice models, panel data models, generalised linear models and their applications. The skills that students will develop in this subject are crucial in any applied work and will constitute an essential ingredient in most jobs in the field of business application, whether in the public or private sector.
|Faculty||Bond Business School|
1. Demonstrate knowledge of linear regression, its maintained assumptions and their relevant statistical properties.
2. Use simple/multiple regression models to interpret the underlying relationships between the variables and evaluate their statistical significance through hypothesis testing.
3. Demonstrate how to determine, vis-a-vis diagnostic statistics, when the maintained assumptions of the linear regression model are violated and critically evaluate how to address the violations so that correct statistical inference can be drawn.
4. Demonstrate the ability to solve business problems using econometrics packages.
5. Demonstrate the ability to produce a written report that demonstrates higher order understanding of key concepts in applied econometrics.
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
Assumed Prior Learning (or equivalent):
|Withdraw – Financial?||12/06/2021|
|Withdraw – Academic?||03/07/2021|
|Withdraw – Financial?||09/10/2021|
|Withdraw – Academic?||30/10/2021|