2246 Ökonometrie II
Nikolas Kuschnig, MSc (WU)
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
17.09.2020 bis 30.09.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Donnerstag 15.10.2020 15:30 - 17:30 Online-Einheit
Donnerstag 22.10.2020 15:30 - 17:30 D4.0.136
Donnerstag 29.10.2020 15:30 - 17:30 D4.0.136
Donnerstag 05.11.2020 15:30 - 17:30 D4.0.136
Donnerstag 12.11.2020 15:30 - 17:30 D4.0.136
Donnerstag 19.11.2020 15:30 - 17:30 Online-Einheit
Donnerstag 26.11.2020 15:30 - 17:30 Online-Einheit
Donnerstag 03.12.2020 15:30 - 17:30 Online-Einheit
Donnerstag 10.12.2020 15:30 - 17:30 Online-Einheit
Donnerstag 17.12.2020 15:30 - 17:30 Online-Einheit
Donnerstag 07.01.2021 15:30 - 17:30 Online-Einheit
Donnerstag 14.01.2021 15:30 - 17:30 D4.0.136
Donnerstag 21.01.2021 15:30 - 17:30 Online-Einheit
Donnerstag 28.01.2021 15:30 - 17:30 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb

The alternative mode for this course will be a mixed form, akin to the Hybrid Mode. Lectures will be held as usual for the appropriate amount of students, with video material (recorded and/or live) available for the rest. Lectures may also be held completely remotely via Teams, if deemed suitable or necessary.

Please note that in this case, the grading scheme may be subject to changes.


Inhalte der LV

This course covers advanced subjects in econometrics, focusing on causal inference and model building. We will cover common problems of regression analysis and potential remedies. Applied examples and assignments will be laid out to use the R language.

The following topics will be covered in this course:

  • Endogeneity (omitted variables, simultaneity, data errors)
  • Instrumental variables (two-stage least squares)
  • Simultaneous equations models (seemingly unrelated regression)
  • Causal effects (prerequisites, model building, diff-in-diff)

Prior knowledge of the following topics is expected:

  • Multivariate regression (application, interpretation)
  • Regression properties (least squares estimation, classical assumptions, estimator properties, Gauss-Markov theorem)
  • Regression inference (hypothesis testing, confidence intervals, model selection)
  • Assumption failures and remedies (heteroskedasticity, serial correlation, multicollinearity)
  • Functional forms (dummy variables, interaction terms, log-transformations)

These are covered in Econometrics I -- it is assumed that you have a solid understanding of them. In addition, it would be beneficial to have working knowledge of R, e.g. from the Statistics with R course.


Lernergebnisse (Learning Outcomes)

After this course you will be equipped to conduct advanced econometric analyses. You will be aware of common pitfalls and how they may be dealt with. That includes a solid understanding of causal inference -- under which conditions it is possible and how these may be induced. You will be able to independently apply your knowledge using R and critically review applied research.


Regelung zur Anwesenheit

Attendance is compulsory.



The course material will be presented in the form of slides, with accompanying assignments for better comprehension throughout. Assignments are designed to use R, will be worked on in groups, and need to be handed in in written form.
The first half of the course will focus more on econometric theory, while the second half will lean towards more practical issues. The midterm in December will cover these theoretical underpinnings. The final exam in the end of January will cover all of the course material.


Leistung(en) für eine Beurteilung

Assessment will be based on three components:

  • 40% group assignments
  • 20% midterm exam
  • 40% final exam

The grading scheme is as follows:

  1. [88, 100]
  2. [75, 88)
  3. [62, 75)
  4. [50, 62)
  5. [0, 50)



1 Autor/in: Hanck et al.

Introduction to Econometrics with R

Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Stark empfohlen (aber nicht absolute Kaufnotwendigkeit)
Art: Buch
2 Autor/in: Wooldrige

Introductory Economics

Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch
3 Autor/in: Hackl

Einführung in die Ökonometrie

Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch
4 Autor/in: Stock and Watson

Introduction to Econometrics

Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Successful completion of Econometrics I is highly recommended.


Erreichbarkeit des/der Vortragenden


For a quick start to using R, see this short introduction. Many of this course's topics are covered in Introduction to Econometrics with R.

For more general and advanced material, see R for Data Science, and Advanced R.

Detailinformationen zu einzelnen Lehrveranstaltungseinheiten

Einheit Datum Inhalte
1 15.10. - 28.01.

The course is on Thursdays from 15:30 until 17:30.

It takes places from the 15th of October until the 28th of January. There are no lessons on the 24th and 31st of December.


Zuletzt bearbeitet: 15.09.2020