Teaching – Current Courses

Lecture

Prof. Dr. Carsten Trenkler

Autumn semester 2022

PhD

Monday, 15:30 to 17:00 (L9, 1–2, 002) and Wednesday, 17:15 to 18:45 (L9, 1–2, 002)

English

E703, E803, E806 Advanced Econometrics I – III

Assignments (30%), presentation (30%), paper (40%)

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The lecture will focus on multivariate time series models. After reviewing a few general concepts from probability theory and time series analysis, we will first deal with stable VAR models and their use for Granger causality, impulse response analysis, historical decompositions, and forecast error variance decompositions. To this end, we will also discuss important issues on asymptotic- and bootstrap-based inference. Depending on time, we very briefly discuss stable infinite-order VARs and VARMA processes.

Then, we deal with univariate unit root processes and introduce the relevant asymptotic approach for this set-up. In a next step, we turn to a treatment of multivariate unit root processes which are assumed to be integrated of order one, I(1). Afterwards, we briefly introduce the concept of cointegration and learn how cointegration can be integrated into VAR framework leading to a so-called vector error correction model (VECM). As cointegration is not that popular anymore in empirical work, we will only summarize how to do appropriate inference in potentially cointegrated VARs with I(1) variables.

If time permits, I would like to give an overview on estimating high-dimensional VARs and factor models in the last part of the course.

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. This year, I will provide a set of recent papers on network analysis using multiple time series approaches. This shall give an impression and overview on a recent strand in the literature.

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.

• Preliminary Syllabus
• Some comments on adjustments for this year's course
• Further material can be found in the course's Ilias group.

Seminar

Applied Econometrics

Prof. Dr. Carsten Trenkler

Autumn semester 2022

Bachelor

Start: 05.09., End: 07.12.

Wednesday 10.15–11.45 in room L7, 3–5, P043

English

Grundlagen der Ökonometrie (Basic Econometrics); Statistik I+II (Statistics I + II)

seminar paper and presentations

6

• Seminar description

Further information will be made available in the Ilias group of the seminar.

Vorlesung und Übung

Grundlagen der Ökonometrie

Prof. Dr. Carsten Trenkler

Frühjahrsemester 2022

Di 13.45 – 15.15, A001 (B6, 23–25, Bauteil A)

8 Übungs­gruppen, Termine und Räume finden Sie im Portal2

Vorlesung und Übung (2+2)

Deutsch

Statistik I+II 

schriftlich (90 Minuten)

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Der Kurs gibt eine Einführung in die wichtigsten Methoden der Ökonometrie. Besprochen werden das multiple Regressions­modell, bedingte Erwartungs­werte und lineare Projektionen, KQ-Schätzer und ihre Eigenschaften, die Grundzüge asymptotischer Theorie, Verzerrung durch ausgelassene Variablen, Restriktions­tests, Modellspezifikation, Modelldiagnose, perfekte und imperfekte Multikollinearität, Kausalitätsanalyse, 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.

Hier finden Sie den Vorlesungs­plan.

Weitere Details (inklusive Vorlesungs­folien) finden Sie auf der Ilias Seite zum Kurs.

Lecture and Tutorial

E806 Advanced Econometrics III (PhD)

Prof. Dr. Carsten Trenkler

Spring Semester 2022

Start: 6.4., End: 2.6.

Wed 8:30 a.m. – 11.45 a.m. P044 (L7, 3–5)

English

Advanced Econometrics I+II

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 contains some empirical questions or coding exercises. You may use any of the following (matrix) programming languages: STATA, R, Matla or Gauss to address the latter types of questions. You will have a week to complete the assignments. The assignments and programming code must be sent by email. Answers will be (partly) discussed in the tutorial sessions.

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In 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. After a general introduction, we fist consider stable ARMA model, then discuss the bootstrap for time series settings, and, finally turn to unit root econometrics.

Further details and course Material will be provided via the Ilias group.
You can also download the syllabus here.

Block-Seminar

Applied Econometrics

Prof. Dr. Carsten Trenkler

Spring Semester 2022

Bachelor

Mon, 14.02.22 & 21.02.22 (Time: 3.30pm-5pm)

Tba

English

Grundlagen der Ökonometrie (Basic Econometrics); Statistik I+II (Statistics I + II)

seminar paper and presentations

6

Each participant conducts an own empirical study in order to become familiar with applied research, what includes the ability to interpret empirical results in a meaningful way. Based on the material covered in the course Grundlagen der Ökonometrie, you will extend your knowledge on econometric models, estimation methods and test procedures in order to solve empirical problems. The seminar topics will refer to the multiple regression model for cross-section data including IV estimation setups as well as to microeconometric, panel data and time series models. Thereby, you should gain a broad overview on the various model classes through your own and your fellow students’ projects.

• Seminar description

Further information will be made available in the Ilias group of the seminar in due time.