Winter Term 2020/21

Students enroll in this colloquium during their Bachelor thesis.

* Due to the currently increasing number of corona infections, the event will take place online via Zoom. This information is not yet reflected on PULS.

Creditable as

• Economics: B.FK.VWL.110, B.KO.PUW.110

You can find further information here.

The course is held in English.

Downloads

• Exam Results
• Time Schedule

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.

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

1. Actively participating in all sessions and complying with all deadlines listed in the schedule.
2. Complete the reading assignments for the “Introduction to Machine Learning” Sessions.
3. Present one empirical application.
4. Complete two empirical problemsets.
5. Complete the final empirical assignment.
6. 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.