Vorlesung + Übung: Ökonometrie - Details

Vorlesung + Übung: Ökonometrie - Details

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Allgemeine Informationen

Veranstaltungsname Vorlesung + Übung: Ökonometrie
Semester WS 2025/26
Aktuelle Anzahl der Teilnehmenden 74
Heimat-Einrichtung Prof. Dr. Florian Diekert - Umweltökonomik
Veranstaltungstyp Vorlesung + Übung in der Kategorie Lehre
Nächster Termin Dienstag, 09.12.2025 15:45 - 17:15, Ort: (J1105)
Voraussetzungen - Elementary probability theory, in particular: concepts of probability and random variables, distribution functions, expectation and variance, basics of hypothesis testing. Linear regression models.
- Completing the module "Einführung in die qualitativen Methoden der empirischen Sozialforschung" by Prof. Dr. Robert Nuscheler is recommended but not required.
- For this course, students are required to bring a laptop to the lectures and tutorials. If they do not have their own, it is possible to rent one from ZEBRA.
Lernorganisation In addition to the content covered in the lecture, the exercise sessions combine interactive in-person tutorials with online R tutorials. These sessions focus on solving exam-relevant problems collaboratively, with a strong emphasis on group work to deepen understanding and practice application. Active participation by students during the lectures, and especially in the tutorials, is expected and required. Furthermore, students may voluntarily work on and submit “Challenges” — take-home exercises in R — which provide the opportunity to earn bonus points for the final exam (Please note that bonus points cannot turn a failing exam into a passing one, and the top grade (1.0) can be achieved without any bonus points).
Leistungsnachweis Exam
Hauptunterrichtssprache englisch
Literaturhinweise Stock, James H., and Mark W. Watson. 2015. *Introduction to Econometrics*, 3rd ed. (For students who already own the book from Prof. Dr. Robert Nuscheler’s lecture).

Stock, James H., and Mark W. Watson. 2020. *Introduction to Econometrics*, 4th ed. Pearson: Boston. [https://ebookcentral.proquest.com/lib/augsburg/detail.action?docID=5834470]

Gertler, Paul J.; Martinez, Sebastian; Premand, Patrick; Rawlings, Laura B.; Vermeersch, Christel M. J. 2016. *Impact Evaluation in Practice*, 2nd ed. Inter-American Development Bank and World Bank: Washington, DC. [https://hdl.handle.net/10986/25030]

Cunningham, Scott. 2023. *Causal Inference: The Mixtape*. [https://mixtape.scunning.com/]

*R-Companion*. [https://www.econometrics-with-r.org/]
ECTS-Punkte 5

Räume und Zeiten

(J1105)
Dienstag: 15:45 - 17:15, wöchentlich (11x)
Dienstag: 17:30 - 19:00, wöchentlich (8x)
Donnerstag: 10:00 - 11:30, wöchentlich (8x)
(tba)
Dienstag: 17:30 - 19:00, wöchentlich (1x)
Donnerstag: 10:00 - 11:30, wöchentlich (1x)
(Zoom (https://uni-augsburg.zoom-x.de/j/67835532729?pwd=cQyYsPl5xv4lUGDVUbXxrCOjFl4JvF.1))
Dienstag: 17:30 - 19:00, wöchentlich (1x)
(Zoom (https://uni-augsburg.zoom-x.de/j/66656662304?pwd=TM3POq1qmainX0o5bb4afbGTwS3ZFO.1))
Dienstag: 17:30 - 19:00, wöchentlich (1x)
(Zoom (https://uni-augsburg.zoom-x.de/j/61853634120?pwd=RFWbGsCRiWOVpmLzMC68xbK1WOPXJj.1))
Donnerstag: 10:00 - 11:30, wöchentlich (1x)
(Zoom (https://uni-augsburg.zoom-x.de/j/66034570660?pwd=KvtbprU3VZpzr2DadTPBaaF46yoZbi.1))
Donnerstag: 10:00 - 11:30, wöchentlich (1x)
(Zoom (https://uni-augsburg.zoom-x.de/j/62111318721?pwd=bQbMFPAAgXiaam56DPcwkbtWZdxZtq.1))
Donnerstag: 10:00 - 11:30, wöchentlich (1x)

Modulzuordnungen

Kommentar/Beschreibung

After successfully completing this module, students are familiar with the statistical foundations of regression analysis. They know the classical assumptions of the linear regression model with independent and identically distributed observations and understand the properties of the Ordinary Least Squares and Maximum Likelihood estimators under these assumptions. Students are able to formulate, conduct, and correctly interpret statistical hypothesis tests within the framework of the linear regression model. They understand the problems that may arise if the classical assumptions about the data-generating process are not met and are familiar with approaches to address these issues. They also know the specific characteristics of time series data and understand the necessary adjustments to the model framework.

Students are familiar with the theoretical framework of regression analysis and can independently carry out regression analyses using the statistical software R. They can interpret the results and formulate and perform hypothesis tests relevant to their research questions. They are able to verify whether the data satisfy the respective model assumptions.

Students are able to comprehend empirical studies, critically evaluate their results, and explain them to others. They can independently apply the methods learned to practical research questions and are capable of conducting simple empirical studies on their own.