The course introduces the student to modern statistics and econometrics. The topics are probability, statistical inference and econometrics. The course is useful for students who want to quickly fill in their background in statistics and econometrics without being burdened by tedious topics in probability and statistics that play no role in econometrics.
Part 1 of the course considers matrix algebra, estimation and inference in the linear regression model, least squares, generalised least squares, instrumental variables and models for panel data.
Part 2 of the course provides an introduction to probability, large-sample theory and maximum likelihood methods in econometrics.
The course is intended for master students in finance and economics with an interest in statistics and econometrics. The course can also be taken by doctoral students.
Assessment
Part 1
Assignments 50%
Exam 50%
Part 2
Assignments 50%
Exam 50%
Enrolment key in Moodle: SE23
Part 1 of the course considers matrix algebra, estimation and inference in the linear regression model, least squares, generalised least squares, instrumental variables and models for panel data.
Part 2 of the course provides an introduction to probability, large-sample theory and maximum likelihood methods in econometrics.
The course is intended for master students in finance and economics with an interest in statistics and econometrics. The course can also be taken by doctoral students.
Assessment
Part 1
Assignments 50%
Exam 50%
Part 2
Assignments 50%
Exam 50%
Enrolment key in Moodle: SE23
- Teacher: Niklas Ahlgren
- Teacher: Alexander Back
- Teacher: Thoi Mai