Winter Term 2019/20
Teaching
BA: Empirical Economics / Econometrics
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| VL (2 SWS) | 14.10.2019 - 07.02.2020 | weekly Monday 14.00 - 16.00 | 3.06.H01 | Dr. Sylvi Rzepka |
| UE 1 (2 SWS) | 14.10.2019 - 07.02.2020 | weekly Tuesday 14.00 - 16.00 | 3.06.H08 | Melina Ludolph |
| UE 2 (2 SWS) | 14.10.2019 - 07.02.2020 | weekly Wednesday 16.00 - 18.00 | 3.06.H08 | Melina Ludolph |
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.
Downloads
Exam
- Written exam (90 min)
Creditable as
- Economics: B.BM.VWL.420, B.VM.VWL.610
- Business Administration: BA-P-602
- "Studiumplus": BA-SK-W-1
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
- Introduction to STATA
- Estimating, testing and predicting in the simple and multiple regression model framework
- Problems and extensions of the multiple regression model
- Policy evaluation
Literature
- Wooldridge, J. (2013): Introductory Econometrics. A Modern Approach. South-Western Cengage Learning.
- Schira, J. (2012): Statistische Methoden der VWL und BWL. Pearson Studium.
- Kohler, U., Kreuter, F. (2008): Datenanalyse mit Stata. Oldenburg Verlag.
BA: Seminar
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| SE | 15.10.2019 - 27.01.2020 | See announcement | Prof. M. Caliendo, Cosima Obst |
Downloads
- Seminar announcement
- Formal requirements (German)
- Template: Declaration of academic honesty (German)
- Template: Declaration of consent (German)
Dates
- 15.10.19: Pre-registration via email.
- 16.10.19: Kick-off Meeting: 2pm (s.t), room 3.07.0.39.
- 23.10.19: Registration via per email with topic preferences.
- 25.10.19: Allocation of the topics.
- 25.10.19: Introductory meeting: 10pm, room 03.01.150.
- 08.11.19 to 10.01.20: Periodic meetings , every Friday, 11am-1pm, room to be announced on Moodle.
- till 10.11.19: Registration on PULS for seminar and portfolio presentation
- 25.11.19: Intermediate presentation: 9am - 6pm, room 3.06.S22.
- 17.01.20: Submission deadline: 12pm, 2x printed; electronic version via email.
- 20.01.20: Allocation of discussants via email.
- 27.01.20: Final presentations and discussions: 9am-6pm, room 3.06.S13/S12
Exam
- Participation in all meetings
- Compliance with all dates and deadlines
- Seminar paper (max. 15 pages)
- Final presentation
- Discussant of another seminar paper at the final presentation
Creditable as
- Economics: BA-S-600, B.VM.VWL.710, B.VM.VWL.410, B.VM.VWL.510, B.VM.VWL.113
Requirements
- BA: Statistics
- BA: Empirical Economics / Econometrics
BA: Colloquium
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| C | 14.10.2019 - 07.02.2020 | Monday 16.00-18.00 | 3.06.S21 | Prof. M. Caliendo |
Students enroll in this colloquium during their Bachelor thesis.
Creditable as
- Economics: B.FK.VWL.110, B.KO.PUW.110
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.
MA: Univariate/Multivariate Time Series Econometrics
| Type | Period | Time | Room | Lecturer |
|---|---|---|---|---|
| LE (2 SWS) | 14.11.2019 - 12.12.2019 | Thursdays see announcement | 3.07.2.10 | Prof. Dr. Marco Caliendo / PD Dr. Till Strohsal |
| A-PR (2 SWS) | 20.11.2019 - 20.12.2019 | Wednesdays see announcement | 3.07.2.10 | Prof. Dr. Marco Caliendo / Thore Schlaak |
| A-PR (2 SWS) | 15.11.2019 - 20.12.2019 | Fridays see announcement | 3.01.1.65a | Prof. Dr. Marco Caliendo / Thore Schlaak |
These are two separate courses held in English, for details see the announcement below.
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Necessary completion for each course
- Written exam (90 min.)
- Active participation and presentation in practical sessions
Creditable as
- MA-W-110
- MA-W-120
Requirements
- Foundations in Mathematics and Statistics are essential. For the course in multivariate time series econometrics, taking univariate time series econometrics is highly recommended.
Content
This course deals with time series econometric methods that are mainly applied in the fields of Macroeconomics and Finance. The lecture and the tutorials will be held in English. Formally, this course consists of two separate courses (univariate and multivariate time series econo- metrics) which will have separate exams at the end of the semester. Models of univariate and multivariate time series with stationary and non-stationary processes will be presented. Students learn methods and tools for analyzing univariate and multivariate time series and apply them in the computer tutorials to recent, real world data.
Topics
Univariate time series models:
- ARMA Processes
- Augmented distributed lag models
- Integrated and cointegrated processes.
Multivariate time series models:
- VAR und VECM
- Identification and analysis of structural shocks: SVAR
Literature
- Enders, W. (2004): Applied Econometric Time Series Analysis. Wiley.
- Hamilton, J.D. (1994): Time Series Analysis. Princetion University Press.
- Kirchgässner, G., Wolters, J., and Hassler, U. (2013): Introduction to Modern Time Series Analysis. Springer.
- Lütkepohl, H. (2007): New Introduction to Multiple Time Series Analysis. Springer.
MA: Advanced Microeconometrics
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| LE (2 SWS) | 14.10.2019 - 14.01.2020 | see Time Schedule | see Time Schedule | Prof. M. Caliendo |
| A-PR (2 SWS) | 05.11.2019 - 28.01.2020 | see Time Schedule | see Time Schedule | Markus Müller, Daniel Rodríguez |
| A-PR (Stata) | 18.10.2019 - 27.01.2020 | see Time Schedule | see Time Schedule | Markus Müller, Daniel Rodríguez |
The course is held in English.
Downloads
Exam
- Written exam
- Active participation during practical sessions.
- Presentation / Term Paper.
Creditable as
- Economics: MA-B-300, MA-600
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: Research Colloquium
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| C | 14.10.2019 - 07.02.2020 | Monday 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
MA: Research Seminar
| Type | Period | Day/Time | Room | Lecturer |
|---|---|---|---|---|
| RS/C | 15.10.2019 - 07.02.2020 | see Announcement | Dr. Sylvi Rzepka, Markus Müller |
This event is held in English.
Download
- Seminar announcement
- Formal requirements (German)
- Template: Declaration of academic honesty (German)
- Template: Declaration of consent (German)
Dates
- 15.10.19: Pre-registration for kick-off meeting
- 16.10.19, 10:00-11:30: Kick-off meeting (room: Haus 7, 2.10)
- 23.10.19, midnight: Official registration via e-mail with 3 ordered preferences of topics
- 25.10.19: Assignment of topics via e-mail
- 25.10.19, 14:00-17:30: Introduction to Machine Learning (room: Haus 7, 2.10)
- 7./8.11.19: Introduction to R Workshop (PCQR)
- 29.11.19, midnight: Block 1: Send your draft slides and office hour sign-up
- 13.12.19, 14:00 - tbd: Block 1: Student Presentations (room: Haus 7, 2.10)
- 20.12.19, 14:00: Problemset 1 due
- 20.12.19, midnight: Block 2: Send your draft slides and office hour sign-up
- 17.01.20, 14:00 - tbd: Block 2: Student Presentations (room: Haus 7, 2.10)
- 24.01.19, 14:00: Problemset 2 due
- 31.01.20, 14:00-15:30: Wrap-up and final discussion (room: Haus 7, 2.10)
- 31.01.20: Empirical assignment sent out
- 7.2.20, midnight: Empirical assignment paper due
Creditable as
- Economics: MA-FK-600, MA-W-210/220
Requirements
- MA: Microeconometrics
- MA: Public Policy Evaluation recommended
Exam
- Actively participating in all sessions and complying with all deadlines listed in the schedule.
- Complete the reading assignments for the “Introduction to Machine Learning” Sessions.
- Present one empirical application.
- Complete two empirical problemsets.
- Complete the final empirical assignment.
- Your grade will be determined by how well you do in your presentation, in participating in the discussion, in the problemsets, and in the final empirical assignment.
Information
Title: "Topics in Machine Learning and Econometrics"
This seminar provides a broad overview of the main concepts of machine learning, especially supervised learning, and how they can enhance causal inference. We will not only discuss recent empirical economics papers applying machine learning methods, but also explore how to implement these methods in R. Students will have the chance to get to know R in a Workshop organized by PCQR and/ or through online courses provided by “Datacamp for the classroom”.
During the semester students will present one empirical application and complete two problemsets. The final assignment will be in the spirit of a Machine Learning Challenge. Throughout the course, students have the chance to practice public speaking and presenting empirical results intuitively as well as getting hands-on experience in R. Furthermore, this course will enable students to follow-up on new developments in this quickly evolving field on their own.