After completing this course participants will

• have the ability to apply and interpret the results of regression analyses
• be familiar with key aspects relevant for the specification of a regression model
• understand the relevance and implications of various assumptions in each step of the analysis
• know why and how specific properties of regression residuals can be tested
• understand the consequences of violations of certain assumptions, and know how to account for them
• be familiar with basic definitions of financial returns, and able to derive and interpret their empirical (dynamic) properties
• know how to distinguish non-stationary from stationary series and how to apply unit-root tests
• understand the purpose and the basic principles of GARCH models, and how to estimate and test such models

Participants are required to attend each class, except for serious illness and/or important private concerns. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

The course is taught using a combination of lectures and practical examples demonstrated in class. The lectures are aimed at establishing a sound understanding of the main ideas and basic principles of econometric methods and analyses. Special emphasis is put on applications using financial data.

• 30% of the final grade is based on the midterm exam.
• 40% of the final grade is based on the final exam.
• 30% of the final grade is based on homework assignments. Homework assignments can be done in groups consisting of up to 3 members. Each member of a group must be able to explain all aspects of an assignment (i.e. group members must not only do parts of an assignment (i.e. must not share the workload); all group members should work jointly on the assignment and must take full responsibility).

Grading scheme:

• 1 if final score >= 0.875
• 2 if final score < 0.875 and >=0.75
• 3 if final score < 0.75 and >=0.625
• 4 if final score < 0.625 and >=0.5
• 5 if final score < 0.5

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Participants should be familiar with the following topics on an undergraduate level:

• Mathematics (e.g. matrix algebra, polynomials, derivatives, integrals, etc.)
• Probability (e.g., random variables and vectors, distributions, conditional probability, expectation/variance/covariance operators, etc.).
• Statistics (e.g., descriptive statistics, sampling distributions, estimation, confidence intervals, hypothesis testing,etc.)
• Computing: R