Type | Period | Day/Time | Room | Lecturer |
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VL (2 SWS) | 02.11.2020 - 12.02.2021 | weekly Monday 14.00 - 16.00 | online event | Dr. Sylvi Rzepka |
UE 1 (2 SWS) | 02.11.2020 - 12.02.2021 | weekly Tuesday 14.00 - 16.00 | online event | Melina Ludolph |
UE 2 (2 SWS) | 02.11.2020 - 12.02.2021 | weekly Wednesday 16.00 - 18.00 | online event | Melina Ludolph |
The course will be held online. All information is available on the Moodle course: https://moodle2.uni-potsdam.de/course/view.php?id=24826, which will be activated at the beginning of the lecture period, with open access during the first week of the semester. From the second week onwards, you can ask the chair's assistant for the password: pohleuempwifo.uni-potsdampde.
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.
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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.
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Type | Period | Day/Time | Room | Lecturer |
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C | 02.11.2020 - 08.02.2021 | Monday 16.00-18.00 | 3.06.S21* | Prof. M. Caliendo |
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.
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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.
You can find further information here.
Type | Period | Time | Room | Lecturer |
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LE (2 SWS) | 19.11.2020 - 04.12.2020 | Thursdays see announcement | 3.06.S14* | Prof. Dr. Marco Caliendo / PD Dr. Till Strohsal |
A-PR (2 SWS) | 20.11.2020 - 04.12.2020 | Wednesdays see announcement | 3.06.S14* | Prof. Dr. Marco Caliendo / Niels Aka |
A-PR (2 SWS) | 20.11.2020 - 04.12.2020 | Fridays see announcement | 3.01.1.65a* | Prof. Dr. Marco Caliendo / Niels Aka |
The course is held in English, for details see the announcement below.
*Due to the currently increasing number of corona infections, the course will take place online via Zoom. This information is not yet reflected on PULS.
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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. Models of univariate time series with stationary and non-stationary processes will be presented. Students learn methods and tools for analyzing time series and apply them in the computer tutorials to recent, real world data.
Topics
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Type | Period | Day/Time | Room | Lecturer |
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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.
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The aim of this lecture is to familiarize participants with microeconometric estimation techniques. The lecture will be complemented by a practical session.
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Type | Period | Day/Time | Room | Lecturer |
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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
Type | Period | Day/Time | Room | Lecturer |
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RS/C | 15.10.2019 - 07.02.2020 | see Announcement | Dr. Sylvi Rzepka, Markus Müller |
This event is held in English.
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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.
University of Potsdam
Chair of Empirical Economics
August-Bebel-Straße 89
D-14482 Potsdam
University Complex III (Griebnitzsee), House 1, Room 3.07
Tel.: +49 331 977-3225
Fax: +49 331 977-3210
E-Mail: empwifouuni-potsdampde