Data on the course

Master’s Programme in Economics (Research Track). Open also to doctoral students in economics.

First autumn term, annually in the second period

After the course, the student should know the properties of the estimators introduced and be able to apply them and the related inferential procedures in empirical work. The course should also give a solid foundation for the study of more specialised microeconometric and time series methods.

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.

Advanced Econometrics 1

Knowledge of R (or some other matrix programming language) is useful.

This course moves beyond the standard linear regression model and conventional least squares estimation. Important econometric estimation principles, such as generalized method of moments and maximum likelihood estimation, are covered and the related statistical inference procedures discussed. Basic concepts of simulation-based methods are also introduced. Specifically, the topics covered in the course include

• Generalized method of moments estimation (basic concepts, asymptotic estimation theory, statistical inference)
• Maximum likelihood estimation (basic concepts, asymptotic estimation theory, statistical inference)
• Simulation methods (Monte Carlo simulations, Bootstrap)

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 (70%) and in the homework assignments (30%).