Master’s Programme in Economics (Research track). Open also to doctoral students in economics.
Basic studies in mathematics and statistics, and familiarity with the linear regression model to the extent covered in a Bachelor-level introductory econometrics course.
Knowledge of R (or some other matrix programming language) is useful.
After the course, the student should
- Know the main properties and limitations of the linear regression model
- Be familiar with the basics of asymptotic analysis
- Be able to employ the linear regression model and related inferential methods in empirical research
Annually in the first period
This course introduces the basic methods used in the linear regression analysis of economic variables. The classical finite sample theory and asymptotic analysis of the linear regression model as well as the necessary methodological tools required for these topics are covered. Specifically, the topics covered in the course include
- Classical finite sample theory in the linear regression model
- The basics of asymptotic theory
- Asymptotic theory in the linear regression model
- Autocorrelation, heteroskedasticity and dynamic regressors
- Specification tests
- Omitted variables, instrumental variables and the two-stage least squares estimator (2SLS)
Lecture slides and other material assigned by the lecturer
All material related to the course is delivered through the Moodle area of the course, which also contains a discussion forum where students can discuss issues related to the course with each other and the teacher.
The grade on a scale from 0 (fail) to 5 is based on the points earned in the final exam. At least 40% of the homework assignments must be completed to take the exam.
The course consists of lectures (24 hours) and exercise sessions (8 hours), where solutions to the homework assignments are discussed. The lectures and exercise sessions are not mandatory. The course is completed by (i) a written final exam and (ii) homework assignments. The homework assignments consist of both analytical and empirical exercises.