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Teaching – Current Courses

Spring Semester 2024

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 2024
Zeit & Ort Vorlesung	Di 13.45 – 15.15, A001 (B6, 23–25, Bauteil A)
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 das multiple Regressionsmodell, bedingte Erwartungswertfunktionen, KQ-Schätzer und ihre Eigenschaften, Restriktionstests, Kausalanalyse (potentielle Ergebnisse, BMU-Annahme, OV-Bias), Modellspezifikation, nichtlineare Modellierungen, IV-Schätzung sowie Zeitreihenanalyse. Neben einer einführenden Betrachtung der theoretischen Aspekte der Methoden, wird vor allem deren Anwendung 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. Weitere Details (inklusive Vorlesungsfolien) finden Sie auf der Ilias Seite zum Kurs.

Type	Lecture and Tutorials
Title	Introduction to Multiple Time Series Analysis
Lecturer	Prof. Dr. Carsten Trenkler
Semester	Spring Semester 2024
Target Audience	Bachelor
Time & Location Lecture	Mon 5:15pm-6:45pm, room 357, L7, 3–5
Time & Location Tutorials	Tue 8:30am-10am, room 003 in L9, 1–2 (bi-weekly, schedule will be announced later)
Course language	English
Prerequisites	Grundlagen der Ökonometrie (Basic Econometrics); Statistik I+II (Statistics I + II)
Grading	Exam (90 minutes, 70%), 2 assignments with two to three problems (30%)
ECTS	6
Course description	The course will provide an introduction to multiple time series analysis with a focus on impulse response analysis using vector autoregressive (VAR) models. We start with a very short recap of univariate time series concepts and then turn to the VAR model framework and estimation. Then, we look into structural VAR (SVAR) models that are commonly applied for impulse response analysis, i.e., the analysis of the effects of so-called structural shocks that are (economically) interpretable. We deal with basic identification schemes to recover the structural shock(s) of interest. Finally, we discuss empirical papers using impulse response analysis. The lectures are accompanied by tutorial sessions that deal with some algebraic issues and, in particular, empirical applications. 'Introduction to Multiple Time Series Analysis' complements well the course 'Time Series and Forecasting' but it can also be taken independently without any problems.
Downloads	You can find the syllabus here. Further information will be made available in the Ilias group as soon as possible.

Notes:

Type	Lecture and Tutorial
Title	E806 Advanced Econometrics III (PhD)
Lecturer	Prof. Dr. Carsten Trenkler
Semester	Spring Semester 2024
Target Audience	PhD
Start/End	Start: 17.4., End: 30.5.
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 dates for holidays: May 10, 8:30–11:45am (L7, 157), May 31, 8:30–11:45am (L7, P043)
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 will be based on the final exam (100 points). You can earn up to 10 bonus points if you submit solutions to the assignments that demonstrate a sufficient attempt to solve problems. To each of the three assignments a pre-announced number of bonus points is allocated. The assignments will mainly involve methodological questions but also contain some empirical questions or coding exercises. You may use any of the following (matrix) programming languages: STATA, R, Matlab, or Gauss to address the latter types of questions. You will usually have a week to complete an assignment. Your solutions and programming code must be sent by email. Answers will be (partly) discussed in the tutorial sessions.
ECTS	5
Course description	Part I is devoted to the analysis of panel data models. Besides discussing fixed- and random effects settings we also look into GMM/IV estimation and dynamic panel models. Part II deals with univariate time series analysis. We start with discussing theoretical foundations of time series analysis and then turn to linear models, including autoregressions. Finally, we deal with non-stationary unit root time series if time permits.
Downloads	The course material will be provided via the Ilias group.