Winter Term 2023/24
Teaching
BA: Empirical Economics / Econometrics
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
VL (2 SWS) | 16.10.2023 - 05.02.2024 | weekly Monday 14.00 - 16.00 | 3.06.H06 | Dr. Katrin Huber |
UE 1 (2 SWS) | 18.10.2023 - 07.02.2024 | weekly Wednesday 12.00 - 14.00 | 3.06.H08 | Sophie Wagner |
UE 2 (2 SWS) | 18.10.2023 - 07.02.2024 | weekly Wednesday 14.00 - 16.00 | 3.06.H08 | Louis Klobes |
UE 3 (2 SWS) | 19.10.2023 - 08.02.2024 | weekly Thursday 10.00 - 12.00 | 3.06.S26 | Sophie Wagner |
The course will be complemented by the Key Skill module B.SK.VWL.210/ B.SK.MET.210 "Einführung in die computergestützte Datenanalyse" which is organized by the Chair of Empirical Social Research (Prof. Dr. Kohler). More information is available here.
Exam
- Written exam (90 min)
Creditable as
- Economics: B.BM.VWL.420, B.VM.VWL.610 (6 ECTS)
- Business Administration: BA-P-602 (6 ECTS)
- "Studiumplus": BA-SK-W-1 (6 ECTS)
Requirements
- BA: Statistics strongly recommended
Content
The aim of this course is to provide the participants with a basic understanding of empirical economics and to give them an introduction to econometrics. Building on the lecture "BA: Statistics" the participants shall be enabled to conduct empirical analysis on their own.
Topics
- Analysis of economic relationships
- Introduction to econometrics
- Estimating, testing and interpreting the simple and multiple regression model framework
- Problems and extensions of the multiple regression model
- Policy evaluation
- Introduction to STATA
Literature
- Schira, J. (2012): Statistische Methoden der VWL und BWL. Pearson Studium.
- Wooldridge, J. (2016): Wooldridge (2016): Introductory Econometrics. A Modern Approach, Cengage Learning, Ohio.
- Kohler, U., Kreuter, F. (2012): Datenanalyse mit Stata. Oldenburg Verlag.
BA: Colloquium
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
C | 17.10.2023 - 06.02.2024 | Tuesday 16.00-18.00 | 3.06.S13 | Prof. M. Caliendo |
Students enroll in this colloquium during their Bachelor thesis.
Creditable as
- Economics: B.FK.VWL.110, B.KO.PUW.110 (6 ECTS)
BA: Introduction to Computer-Based Data Analysis (Key Skill)
The course is provided by the Chair of Methods of Empirical Social Research (Prof. Dr. Kohler).
More information can be found on PULS and on the homepage of the Chair of Methods of Empirical Social Research of Prof. Dr. U. Kohler.
BA: Self-reflection and Planing (Key Skill)
You can find further information here.
BA: Introduction to machine learning with applications in R
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
SE | 10.11.2023 | Friday 12.00 - 18.00 | 3.06.S26 | Prof. Dr. Schnitzlein |
SE | 11.11.2023 | Saturday 10.00 - 16.00 | 3.06.S26 | Prof. Dr. Schnitzlein |
SE | 08.12.2023 | Friday 12.00 - 18.00 | 3.06.S26 | Prof. Dr. Schnitzlein |
SE | 09.12.2023 | Saturday 10.00 - 16.00 | 3.06.S26 | Prof. Dr. Schnitzlein |
Anrechenbar als
- BVMVWL420 (6 ECTS)
Kursbeschreibung:
Diese Vorlesung bietet eine Einführung in die Grundlagen des maschinellen Lernens unter Verwendung der Programmiersprache R. Das maschinelle Lernen ist eine Schlüsselkomponente in der heutigen datengetriebenen Welt und findet in verschiedenen Anwendungen wie der Datenanalyse, Prognose, Klassifikation und Clustering Anwendung.
In diesem Kurs werden die Studierenden in die Grundkonzepte des maschinellen Lernens eingeführt, beginnend mit den verschiedenen Arten von Lernparadigmen, einschließlich überwachtem und unüberwachtem Lernen. Wir werden uns auf praktische Anwendungen konzentrieren und Schritt für Schritt die wichtigsten Techniken wie lineare Regression, Entscheidungsbäume, k-Nearest Neighbors und neuronale Netze in R implementieren und anwenden.
Am Ende des Kurses werden die Studierenden in der Lage sein, maschinelles Lernen in R anzuwenden, um reale Datenprobleme zu lösen, Modelle zu entwickeln und die Ergebnisse zu interpretieren. Dieser Kurs bietet eine solide Grundlage für weiterführende Kurse im Bereich des maschinellen Lernens und der Datenanalyse.
Voraussetzungen: Grundlegende Kenntnisse der Programmiersprache R und grundlegende Statistikkenntnisse sind hilfreich, sind aber nicht zwingend erforderlich.
Qualifikationsziele:
Die Studierenden verfügen über ein solides Verständnis der grundlegenden Konzepte des maschinellen Lernens entwickeln, einschließlich der Unterscheidung zwischen überwachtem und unüberwachtem Lernen sowie der verschiedenen Arten von Lernalgorithmen. Die Studierenden sind in der Lage, Daten in R effizient zu importieren, zu bereinigen und zu explorieren, um wichtige Muster und Zusammenhänge zu identifizieren, die für das maschinelle Lernen relevant sind.
Literatur:
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021): An Introduction to Statistical Learning with Applications in R, Second Edition, Springer.
MA: Advanced Microeconometrics
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
LE (2 SWS) | 16.10.2023 - 23.01.2024 | see Time Schedule | see Time Schedule | Prof. M. Caliendo |
A-PR (2 SWS) | 14.11.2023 - 30.01.2024 | see Time Schedule | see Time Schedule | Aiko Schmeißer |
A-PR (Stata) | 31.10.2023 - 29.01.2024 | see Time Schedule | see Time Schedule | Aiko Schmeißer |
The course is held in English.
Downloads
Exam
- Written exam
- Active participation during practical sessions
- Oral presentations
Creditable as
- Economics: MA-B-300, MA-600 (9 ECTS)
Content
The aim of this lecture is to familiarize participants with microeconometric estimation techniques. The lecture will be complemented by a practical session.
Outline
- Multiple Regression
- Instrumental Variables
- Panel Data Methods
- Limited Dependent Variables
Literature
- Wooldridge, J. (2013): Introductory Econometrics. A Modern Approach. South-Western Cengage Learning.
- Cameron, C., and P. K. Trivedi (2005): Microeconometrics. Methods and Applications. Cambridge University Press, New York.
- Greene, W. H. (2012): Econometric Analysis. Pearson, Massachusetts.
- Kohler, U., Kreuter, F. (2008): Datenanalyse mit Stata. Oldenburg Verlag.
- Cameron, C., and P. K. Trivedi (2010): Microeconometrics Using Stata, Stata Press, College Station, Texas.
MA: Machine Learning
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
SE | 24.11.2023 | Friday 9.00 - 18.00 | online event | Dr. Marica Valente |
SE | 01.12.2023 | Friday 9.00 - 18.00 | online event | Dr. Marica Valente |
SE | 04.12.2023 | Monday 9.00 - 18.00 | online event | Dr. Marica Valente |
SE | 19.01.2024 | Friday 9.00 - 18.00 | online event | Dr. Marica Valente |
This event is held in English.
Creditable as
- MA-W-210, MA-W-220, MA-M-320 (6 ECTS)
Content
- Statistics, econometrics and machine learning.
- Draw contrasts with traditional approaches.
- How to use machine learning methods for prediction?
- How to use machine learning tools in R?
- Tree-based methods in R.
- Parametric methods.
- Analyze regression-based methods in R
- How to conduct empirical research?
- How to write an empirical paper?
Exam
- Oral exam (50%)
- Term paper (50%)
MA: Research Seminar
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
RS/C | 17.10.2023 - 06.02.2024 | see Syllabus | Prof. M. Caliendo, Dr. Katrin Huber, Aiko Schmeißer |
This event is held in English.
Downloads
Termine
This is a tentative schedule. Final schedule depends on enrollment.
- 17.10.23, 4 - 6pm, Room S13: Kick-off meeting.
- 20.10.23, midnight: Deadline for registration via email.
- 24.10.23, 4 - 7pm, Room S13: Lecture and Q&A: How to write a referee report? How to give a research presentation?
- 31.10.23, midnight: Referee report 1 due.
- 07.11.23, 4 - 7pm, Room S13: Presentation and Discussion 1.
- 14.11.23, midnight: Referee report 2 due.
- 21.11.23, 4 - 7pm, Room S13: Presentation and Discussion 2.
- 28.11.23, 4 - 7pm, Room S13: Lecture and Q&A: How to come up with a research idea? How to write a research proposal/paper?
- 12./13./14.12.23 (tbd), Room tbd: First presentation of research idea.
- Optionally: January Discussion of research ideas in additional session or individual office hours.
- 23.01.24, 4 - 7pm, Room S13: Final presentation of research idea.
- Deadline tba Research proposal due.
Anrechenbar als
- Economics: MA-FK-600, MA-W-210/220 (6 ECTS)
Voraussetzungen
- MA: Microeconometrics
- MA: Public Policy Evaluation recommended
Zu erbringende Leistung
The final grade will be awarded based on the performance in the 2 referee reports, in 1-2 presentations/discussions, and the research proposal.
Inhalt
Title: "DIY: Research Idea Development Seminar"
The main aim of this do-it-yourself (DIY) research seminar is to help you to develop and work on your own research idea. On the way you will learn some essential research skills, e.g. refereeing a paper, developing a research outline or presenting research ideas and work-in-progress in front of other researchers.
All ideas in the field of Labor Economics, Policy Evaluation, Population Economics or related areas are welcome. Data exploration, acquisition, and/or generation can be part of the research development process. We will also support you in case you plan to request access to survey data (e.g. SOEP, BIBB BAuA) or admin data (e.g. IAB FDZ data).
MA: Research Colloquium
Type | Period | Day/Time | Room | Lecturer |
---|---|---|---|---|
C | 17.10.2023 - 06.02.2024 | Tuesday 18.00 - 20.00 | 3.06.S13 | Prof. M. Caliendo |
Students enroll in this colloquium during their Master thesis.
The event is held in English.
Creditable as
- Economics: MA-F-100, MA-FK-600 (3 ECTS)