Qualification Course in Econometrics

About

The course consists of a limited number of lectures, a larger number of teacher-made but self-organized exercises and a lot of independent work and self-study. Learning how to apply econometrics to interesting economic problems quite naturally entails working with economic data. Therefore, a large part of the course focuses on acquainting students with the R programming language, the success of which largely depends on the time and effort the students spend on it. The final learning outcome is therefore closely linked to the students’ ability to work independently and thoroughly with the supplied material.

The course focuses on introducing the linear regression model for data analysis within economics. Emphasis is on the statistical theory behind econometrics, understanding the nature of economic data, and the applications of econometrics to real-world problems. The latter emphasizes a focus on the interpretation of statistical results and a discussion on possible limitations or issues with the chosen application. More formally, this requires a thorough understanding of the assumptions underlying the linear regression model and what to do when these assumptions are violated.

The course is very much an applied econometrics course in the sense that it focuses on using and discussing which econometric approach would best uncover the causal relationship of interest, to a larger extent than deriving properties of estimators (or similar).

A natural part of applying statistical methods to some real-world problem is working with data. Therefore, parts of the curriculum focus explicitly on good practices when managing and collecting economic data. Furthermore, the course continuously works with practical data examples, as these are a necessity when trying to infer anything meaningful about some economic phenomenon.

Course leader

Carl-Emil Pless

Target group

Bachelor

Fee info