Lectures of the Institute of Econometrics and Data Science

Area of Expertise in Business Administration and Economics

• Bachelor Seminar on Data Science & Econometric Methods (273034)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme, Sönksen
  Contents:	Students learn how to write a scientific paper in the fields of data science or econometrics and how to present research results in a convincing and scientifically accurate manner. Students deepen their methodological skills and are prepared for writing their final theses.
  Comments:	Details regarding registration procedures and the schedule will be announced on the institute's website. Examiner: Prof. Dr. Sönksen

• Introduction to the Scientific Work at the Institute of Econometrics and Data Science (273035)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme
  Contents:	Introduction to Programming with Python Introduction to LaTeX Academic Writing and Integrity

• Introduction to Machine Learning (273037)

  	Time and room:	Lecturer:
  	Thu. 09:15 - 10:45 | I-301	Sönksen
  Contents:	This course provides students with an introduction to machine learning methods. It deals with regression and classification settings and covers the following topics: difference between econometrics and machine learning learning theory (e.g. bias-variance trade-off) local prediction methods shrinkage and feature selection techniques classification and regression trees
  Literature:	Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)

• Exercise Introduction to Machine Learning (273038)

  	Time and room:	Lecturer:
  	Fri. 09:15 - 10:45 | I-301	Itzen

Area Data Science and Applied Econometrics

• Introduction to the Scientific Work at the Institute of Econometrics and Data Science (373030)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme
  Contents:	Introduction to Programming with Python Introduction to LaTeX Academic Writing and Integrity

• Master Seminar on Data Science & Econometric Methods (373031)

  	Time and room:	Lecturer:
  	Block course	Itzen, Krumme, Sönksen
  Contents:	Students learn how to write a scientific paper in data science or econometrics and to present their results convincingly and in a scientific way. Students deepen their methodological skills and are prepared for writing their master's theses.

• Causal Machine Learning (373032)

  	Time and room:	Lecturer:
  	Fri. 09:15 - 10:45 | II-013	Sönksen
  Contents:	This course combines the flexibility of machine learning methods with econometrics and covers the following topics: a short overview of machine learning measures of causal effects (e.g., average treatment effects) machine learning techniques to estimate causal effects (e.g., causal forests)
  Literature:	Applied Causal Inference Powered by ML and AI, Chernozhukov/Hansen/Kallus/Spindler/Syrgkanis

• Advanced Predictive Methods (373033)

  	Time and room:	Lecturer:
  	Thu. 11:00 - 12:30 | I-332	Sönksen
  Contents:	This course familiarizes students with more elaborate prediction techniques. It covers the following topics: learning theory random forests boosting and bagging artificial neural networks support vector machines
  Literature:	Elements of Statistical Learning (Friedman & Tibshirani)

• Exercise Advanced Predictive Methods (373034)

  	Time and room:	Lecturer:
  	Fri. 11:00 - 12:30 | II-013	Sönksen

• Finance Research Seminar (77782)

  	Time and room:	Lecturer:
  	Wed. 11:00 - 12:30 | I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Contents:	External guests present their latest research

Kompetenzbereiche Betriebs- und Volkswirtschaftslehre

• Ökonometrie (273006)

  	Termine:	Lehrpersonen:
  	Do. 14:30 - 16:00 | I-301	Less
  Inhalt:	Die folgenden Kursinhalte sind geplant, können aber je nach zeitlichem Verlauf variieren: Motivationen für das lineare Regressionsmodell Schätzung und Inferenz Endogenität: Ursprünge und Lösungen Das verallgemeinerte lineare Regressionsmodell Instrumentenvariablen Das Verständnis für die theoretischen Konzepte aus der Vorlesung wird in der Übung gefördert. Dies geschiet u.a. durch den Einsatz statistischer Software. Die Übung ist somit ein wichtiger Bestandteil des Moduls.
  Literatur:	Angrist, J. und Pischke, J.-S. (2015), Mastering Metrics: The path from cause to effect, Princeton University Press, Princeton, New Jersey. Hansen, B. (2022), Econometrics, Princeton University Press, Princeton, New Jersey. Stock, J.H. and M.W. Watson (2007): Introduction to Econometrics, 3rd ed. Pearson
  Bemerkungen:	Materialien werden sukzessive auf Stud.IP zur Verfügung gestellt.

• Übung zu Ökonometrie (273033)

  	Termine:	Lehrpersonen:
  	Di. 07:30 - 09:00 | I-301 (Gruppe 1)	Itzen
  	Do. 07:30 - 09:00 | I-301 (Gruppe 2)	Itzen
  	Mi. 07:30 - 09:00 | I-063 (Gruppe 3)	Krumme