DATA ANALYTICS (4 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – explain the mathematical foundations, statistical foundations, dealing with data, and overview of classical linear regression model; LO2 – apply the classical linear regression model, its assumption and diagnostic tests; LO3 – apply the univariate time-series modelling and forecasting, multivariate models, and modelling long-run relationships in Finance; LO4 – apply the modelling volatility and correlation, switching and state space models, and panel data; LO5 – analyze limited dependent variable models, simulation methods, econometric techniques, and conduct empirical research or doing a project or dissertation in Finance.

Topics:

1. Introduction and mathematical foundations, statistical foundations, and dealing with data;
2. A Brief Overview of the Classical Linear Regression Model;
3. Further Development and Analysis of the Classical Linear Regression Model;
4. Classical Linear Regression Model Assumptions and Diagnostic Tests;
5. Univariate Time-Series Modelling and Forecasting;
6. Multivariate Models;
7. Modelling Long-Run Relationships in Finance;
8. Modelling Volatility and Correlation;
9. Switching and State Space Models;
10. Panel Data;
11. Limited Dependent Variable Models and Simulation Methods;
12. Additional Econometric Techniques for Financial Research;
13. Conducting Empirical Research or Doing a Project or Dissertation in Finance.