Applied Econometrics

  • Type: Vorlesung (V)
  • : Master
  • Semester: WS 22/23
  • Place:

    10.11 Sitzungssaal Hauptgebäude (R223)

  • Time:

    Wednesday, 14:00 - 15:30, weekly

  • Lecturer:

    Prof. Dr. Fabian Krüger

  • SWS: 2
  • Lv-No.: 2520020


Applied econometrics is concerned with answering causal questions (e.g., "How does an internship affect a person's future wage?") and making predictions (e.g., "What is the expected rental price for an apartment, given its size and location?"). This course presents econometric methods for these tasks, with an emphasis on causal inference.

The course has two main parts: (1) Conditional expectation and regression, and (2) Treatment effects and causality. Part (1) reviews important concepts, including the best linear predictor, least squares estimation, and robust covariance estimation. Part (2) introduces the potential outcomes framework for causal inference and discusses research strategies like randomized trials, instrumental variables, and regression discontinuity. For each part, we discuss econometric methods and theory, empirical examples (e.g., recent research papers), and R implementation.

Learning goals

At the end of the course, students are able to assess the properties of various econometric estimators and research designs, and to implement econometric estimators using R software.

Work effort

Total workload for 4.5 ECTS: approx. 135 hours
Attendance time: 30 hours
Self-study: 105 hours




Angrist, J.D., and J.-S. Pischke (2009): Mostly Harmless Econometrics. Princeton University Press.

Cattaneo, M.D., N. Idrobo and R. Titiunik (2020): A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.

Hansen, B. (2022): Econometrics. Princeton University Press.

DiTraglia, F.J. (2021): Lecture Notes on Treatment Effects. Course notes, available at