Teaching – Current Courses
Spring Semester 2026
Grundlagen der Ökonometrie (Bachelor)
Hinweise:
- Beachten Sie bitte, dass die Übungen in der ERSTEN Vorlesungswoche beginnen!
Art der Veranstaltung Vorlesung und Übung Titel der Veranstaltung Grundlagen der Ökonometrie Dozent Prof. Dr. Carsten Trenkler Semester Frühjahrsemester 2026 Zeit & Ort Vorlesung Di 13.45 – 15.15, SN 163 (Schloss) Zeit & Ort Übung 12 Übungsgruppen, Termine und Räume finden Sie im Portal2 Methode (Stunden pro Woche): Vorlesung und Übung (2+2) Kurssprache Deutsch Voraussetzungen Statistik I+II Prüfung schriftlich (90 Minuten) ECTS 6 Kursbeschreibung Der Kurs gibt eine Einführung in die wichtigsten Methoden der Ökonometrie. Besprochen werden Grundlagen der Kausalanalyse (randomisierte Kontrollexperimente, potentielle Ergebnisse, BMU-Annahme), das multiple Regressionsmodell, bedingte Erwartungswertfunktion, KQ-Schätzer und seine Eigenschaften, Inferenz, nichtlineare Modellierungen, Instrumentalvariablenschätzung, sowie Zeitreihenanalyse. Neben der Diskussion der konzeptionellen Grundlagen und der Methoden, wird die Anwendung der Methoden demonstriert und die empirisch relevanten Aspekte diskutiert. Die Vorlesung wird durch methodische und empirische Übungen im PC-Pool begleitet. Downloads Hier finden Sie den Vorlesungsplan (PDF, 76 kB).
Weitere Details (inklusive Vorlesungsfolien) finden Sie auf der Ilias Seite zum Kurs.
Applied Econometrics (Bachelor, Seminar)
Type Seminar Title Applied Econometrics Lecturer Prof. Dr. Carsten Trenkler Semester Spring Semester 2026 Target Audience Bachelor Start/ End: Start: 05.02., End: 27.05. Time & Location: Wednesday 8.30–10a.m. in room 003, L9, 1–2 Course language English Prerequisites Grundlagen der Ökonometrie (Basic Econometrics); Statistik I+II (Statistics I + II) Examination seminar paper and presentations ECTS 6 Course description You will conduct your own empirical study to gain hands-on experience in applied research, including the ability to interpret empirical findings in a meaningful way. Building on the material covered in Grundlagen der Ökonometrie, you will further develop your understanding of econometric models, estimation methods, and testing procedures used to address empirical questions. The seminar topics focus on the multiple regression model for cross-sectional data, including instrumental variable (IV) estimation frameworks, as well as microeconometric, panel data, and time series models. In addition, some projects will specifically examine experimental data and the use of shrinkage estimators. Through your own project and those of your peers, you will gain a comprehensive overview of various model classes and econometric methods. Registration December 1 – 7, 2025 (general registration week for seminars) Downloads Further information will be made available in the Ilias group of the seminar.
E823 Advanced Time Series Analysis (PhD)
Type Lecture Title E823 Advanced Time Series Analysis Lecturer Prof. Dr. Carsten Trenkler Semester Spring Semester 2026 Target Audience PhD Start/ End Start: 11.02., End: 26.03. Time & Location Wednesday, 10:15 to 11:45am (L7, 3–5, P043) and Thursday, 10:15 to 11:45am (L7, 3–5, P043) Course language English Prerequisites E703, E803, E806 Advanced Econometrics I – III Examination Assignments (30%), presentation (30%), paper (40%) ECTS 9 Course description The course will focus on multivariate time series models. After reviewing a few general time series concepts, we will first deal with stable VAR models and their use for Granger causality, impulse response analysis, and forecast error variance decompositions. To this end, we will also discuss important issues on asymptotic- and bootstrap-based inference. Then, we deal with treatment of multivariate unit root processes.
The course both addresses asymptotic analyses as well as implementation issues. Accordingly, tutorial sessions are also devoted to coding and empirical problems besides addressing theoretical problems.
In the last part of the course, participants introduce or discuss in more details (further) model classes by giving presentations and writing a paper.
Our course is complementary to the course offered by Matthias Meier. While the latter course focus on structural modeling approaches from an applied macro perspective, we take an econometric approach on multiple time series frameworks.
Downloads - Preliminary Syllabus (PDF, 69 kB)
- Further material can be found in the course's Ilias group once set up.
E806 Advanced Econometrics III (PhD)
Notes:
Type Lecture and Tutorial Title E806 Advanced Econometrics III (PhD) Lecturer Prof. Dr. Carsten Trenkler Semester Spring Semester 2025 Target Audience PhD Start/ End Start: 15.4., End: 28.5. Time & Location Lecture Wed 10:15 a.m. – 11.45 a.m., Thur 8:30 a.m. – 10:00 a.m. P044 (L7, 3–5)
Alternative date for holiday (14.5.): will be announced later
Time & Location Tutorial Thur 10:15 a.m. – 11:45 a.m. P044 (L7, 3–5) Course language English Prerequisites Advanced Econometrics I+II Homework and grading Grading for this course is based on the final exam (100 points). In addition, you can earn up to 10 bonus points by submitting solutions to the assignments that demonstrate a serious attempt at solving the problems. A pre-announced number of bonus points is allocated to each of the three assignments. The assignments will primarily cover methodological questions but will also include some empirical tasks and coding exercises. For the latter, you may use any of the following programs or programming languages: STATA, R, or MATLAB. You will typically have one week to complete each assignment. Your solutions, including any programming code, must be submitted by email. Selected answers will be discussed in the tutorial sessions. ECTS 5 Course description Part I is devoted to the analysis of panel data models. In addition to discussing fixed and random effects specifications, we also examine GMM/IV estimation as well as dynamic panel models. Part II focuses on univariate time series analysis. We begin by outlining the theoretical foundations of time series analysis and then turn to linear models, including autoregressive models. Finally, time permitting, we address non-stationary time series models with unit roots. Downloads The course material will be provided via the Ilias group.