eventIntroduction to Econometrics [Introduction to Econometrics] (V)
person Boris Hirsch

Next appointment: 13. October at 14:15

Dates

weekly | Tuesday | 14:15 - 15:45 | 12.10.2026 - 29.01.2027 | C 3.120 Seminarraum | Start 1. lecture week

Curriculum context

Written academic performance under supervision (60 Minutes)
Handwritten in examination rooms at Leuphana
Date of assessment: Tuesday, 16.02.2027, 14:15, room C HS 2
Resit date: : Keine selbständige Anmeldung zum Wiederholungstermin möglich. info_outline
Tuesday, 23.03.2027, 12:15, room C HS 3, C HS 4

Präsenzklausur

Anzeige von Anmeldebeginn und -ende systembedingt. Selbständige Anmeldung nur zum Prüfungstermin und nicht zum Wiederholungstermin möglich.

Organizational information

Lecture
Vollständig Präsenz
2
centralized lottery procedure with participant limit
70

Registration

centralized lottery procedure with participant limit

Registration does not begin until 02.10.2026 at 08:00. It ends on 12.10.2026 at 23:59.

The registration is restricted to the following fields or courses of study:

  • Leuphana Bachelor / Major Economics / alle Semester
  • Leuphana Bachelor / Major Studium Individuale / alle Semester
  • Leuphana Bachelor / Minor Studium Individuale / alle Semester

Persons

Content

Englisch
Introduction to Econometrics
Guest auditor program

This course provides an overview of regression analysis. It centres around the question of how to identify causal effects from linear regression models that are estimated using the ordinary least squares (OLS) estimator. The main focus is to discuss the estimation of causal effects under mild identifying assumptions rather than the properties of the OLS estimator under ideal conditions that are of little practical relevance.

Contents:
1. About this course
2. The simple linear regression model
3. Inference in the simple linear regression model
4. The multiple linear regression model
5. Inference in the multiple linear regression model
6. Nonlinear regression functions
7. The validity of regression analyses

Students not only learn how to apply OLS regression appropriately, but also learn how to assess and scrutinise empirical analyses that use it and about its strengths and weaknesses. Moreover, students acquire the relevant skills to use OLS regression in own empirical analyses, e.g. in later empirical research projects or in their bachelor thesis.

Course requirements:
Successful completion of the courses Mathematics I/II and Statistics I/II

Evaluation

This course has not been registered for teaching evaluation yet.

Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html

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