105.628 Econometrics for Business Informatics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to fit a linear regression model for a given data set and in this context to understand corresponding software output and to test statistical hypothesis.

Subject of course

Introduction to econometric models and methods. Linear regression and least-squares estimation, selected applied problems.

Teaching methods

Weekly alternating lecture and exercise sessions.

Mode of examination


Additional information

The course language is English. The course consists of a lecture and an excercise part which will roughly alternate weekly. There will be 3 different groups for the exercise part, registration is through TISS. The homework problems for the exercise sessions should be marked in TUWEL. The course starts for all students on Wednesday, March 3 at 2.00pm in ZOOM:

Meeting ID: 975 6972 1138




Course dates

Wed14:00 - 16:0003.03.2021 - 30.06.2021FH 8 Nöbauer HS - MATH Econometrics for Business Informatics
Econometrics for Business Informatics - Single appointments
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Examination modalities

Students have to prepare exercise problems and pass a midterm and a final exam.

Course registration

Use Group Registration to register.

Group Registration

GroupRegistration FromTo
Exercise Section Group 101.02.2021 20:0007.03.2021 23:59
Exercise Section Group 201.02.2021 20:0007.03.2021 23:59
Exercise Section Group 301.02.2021 20:0007.03.2021 23:59


Study CodeSemesterPrecon.Info
066 926 Business Informatics


  • Wooldridge (2013) Introductory Econometric: A Modern Approach.
  • Ruud (2000) An Introduction to Classical Econometric Theory.
  • Verbeek (2012) A Guide to Modern Econometrics.
  • Greene (2011) Econometric Analysis.
  • Kleiber & Zeileis (2008) Applied Econometrics in R.

Previous knowledge

Mathematics (in particular linear algebra) and statistics at bachelor level.