Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
15.8.2019 at 09:00 - 2.10.2019 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Thu 5.9.2019
14:15 - 15:45
Fri 6.9.2019
12:15 - 13:45
Thu 12.9.2019
14:15 - 15:45
Fri 13.9.2019
12:15 - 13:45
Thu 19.9.2019
10:15 - 11:45
Thu 19.9.2019
14:15 - 15:45
Fri 20.9.2019
12:15 - 13:45
Thu 26.9.2019
14:15 - 15:45
Fri 27.9.2019
12:15 - 13:45
Thu 3.10.2019
10:15 - 11:45
Thu 3.10.2019
14:15 - 15:45
Fri 4.10.2019
12:15 - 13:45
Thu 10.10.2019
10:15 - 11:45
Thu 10.10.2019
14:15 - 15:45
Fri 11.10.2019
12:15 - 13:45
Thu 17.10.2019
10:15 - 11:45
Fri 18.10.2019
12:15 - 13:45
Fri 25.10.2019
10:15 - 11:45
Fri 13.12.2019
10:00 - 12:00

Description

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.