This course covers the foundations of the subject of Econometrics: Causality, correlation, assumptions of the linear regression model, OLS estimation, asymptotic tests, mis-specification, outliers, and heteroskedasticity. This course provides the basic toolkit to assess (multivariate) regression results. Several applications of the methods learned will be covered in the course. Empirical exercises will be worked out in groups. Students are free to learn and use any statistical software, but only R will be introduced and used in class.

The following modules will be covered in this course. They are based on (but not identical with) Chapters 1 through 8 of Jeffrey Wooldridge's textbook “Introductory Econometrics: A Modern Approach.”

1. Introduction to the Subject of Econometrics
2. Simple Linear Regression
3. Multiple Linear Regression
4. Testing and Inference
5. More on Multiple Regression
6. Heteroskedasticity

Prior knowledge of the following topics is expected:

• Basic knowledge of Linear Algebra
• Basic knowledge of Statistics and Probabilities

Working knowledge of the contents covered in, e.g., the C.B.K. lectures “Mathematics” and “Statistics” is enough to fulfill this requirement.

After this course, you

• will have a solid understanding of basic Econometric methods,
• will be able to apply this knowledge using R,
• will be equipped to understand simple empirical applications using econometric methods,
• will be aware of some issues that can arise in empirical work and how to deal with them, and
• will be well-equipped to continue studying Econometrics and learn about Causal Inference in subsequent courses.

Attendance is compulsory. Students are allowed to miss up to two units.

The course consists of 

• Lectures with focus on econometric theory,
• examples of applications during the lectures,
• and a group assignment where you apply your knowledge from the course.

Grade components:

• Midterm exam (40%)
• Final exam (40%)
• Assignments (10%)
• Participation (10%)

A positive combined exam mark (average of midterm and final) is required to be positive in the course.

The grading scheme is:

1. [87.5, 100]
2. [75, 87.5)
3. [62.5, 75)
4. [50, 62.5)
5. [0, 50)

Basic knowledge of statistics, mathematics and matrix algebra. Successful completion of the C.B.K. courses “Mathematics” and “Statistics” is recommended.

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