Master’s Programme in Economics (Research track). Open also to doctoral students in economics.
Advanced Econometrics 1 and 2
Knowledge of R (or some other matrix programming language) is useful.
After the course, the student should
- Know the basic properties of the time series models and the related methods introduced
- Be able to critically follow empirical research that employs them
- Be able to apply them in empirical research
- Have the basic knowledge for more advanced methodological and applied studies in time series econometrics
Annually in the third period
This course covers a number of models and methods employed in time series econometrics. The emphasis is on univariate models, but vector autoregressive models are also discussed. Specifically, the topics covered on the course include the following:
- Basic time series concepts
- Methods for stationary univariate data: ARMA models, ARCH models
- Nonstationarity (unit roots, cointegration)
- Vector autoregressive models
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.