Advanced Statistics

Course Description:

This course covers the mathematical foundations of statistical methods and models commonly used in multivariate data analysis, with a focus on linear modeling techniques. Emphasis is placed on both theoretical understanding and practical application.

An application-oriented component introduces students to data analysis and interpretation using the statistical software R.

Topics include:

1. Multiple Linear Regression (Ordinary and Generalized Least Squares)

2. Generalized Linear Models (Logit and Probit models)

3. Instrumental Variables Regression (omitted variable bias, validity and exogeneity of instruments)

The course is designed to provide students with a solid foundation for applying statistical models to real-world data, particularly in the context of finance and economics.

1. Examination (80%)

A written examination will be held at the end of the course. If the number of enrolled students is fewer than 10, the examination may be conducted in oral format instead.

• The exam assesses theoretical understanding, mathematical derivations, and conceptual knowledge of the models covered in the course.

A resit is available and will follow the same format - written or oral, depending on the number of students and will again account for 80% of the final grade.

2. Weekly Computer Exercises (20%)

Students are required to complete weekly compulsory computer assignments, which involve data analysis in R.

• These assignments are designed to reinforce practical skills and the application of statistical methods.

• The combined performance on these exercises contributes 20% to the final grade.

Completion of computer exercises is mandatory to pass the course.