4408081: Advanced Mathematics and Statistics

Description

This course will cover:

• Expansions of the simple linear regression model to multiple linear regressions
• Classical linear regression model assumptions and diagnostic tests
• Panel data
• Optimization in finance
• Simulational methods in finance

Learning Outcomes

Students...

• understand the generalisation from single to multiple regressions.
• can estimate and conduct statistical inference in multiple regression models
• understand the concept of test size and goodness of fit statistics
• comprehend quantile regressions
• understand the different statistical distributions of statistical tests
• comprehend the assumptions of linear regression models, can test for them and correct their models
• understand the structure of panel data and the relevant techniques to estimate panel data models
• can estimate fixed-effect and random-effect models
• are able to conduct statistical inference in panel data models
• understand the different optimization procedures used in finance
• can apply different optimization procedures to problems in finance
• understand the different forms and uses of simulation in finance
• apply simulational methods to practical problems in finance

Admission Requirements

• Students should have a working understanding of simple linear regressions, both in a theoretical manner as well as regarding its practical implementations (estimation and statistical inference) in R or EViews (or any other appropriate statistical programme)
• Students should have an understanding of single and multi variable calculus, matrices, probability and probability distributions, and descriptive statistics

Literature

Brooks, C. (2014). Introductory econometrics for finance (3rd ed.). Cambridge: Cambridge University Press.