Seminar für empirische Wirtschaftsforschung (Prof. Winter)
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Inhaltsbereich

Causal Inference for Policy Evaluation

Lecturers

Dr. Andreas Steinmayr

Audience

Students in the Master of Science in Economics, Master in Quantitative Economics, and doctoral students.

Schedule

Tuesday, 23.04.2019 - 21.05.2019, 08:00 - 12:00; Ludwigstr. 33, 444/4th floor (Economic History Library

Presentations:

Friday, 07.06.2019, 08:00 - 18:00;  Ludwigstr. 33, 444/4th floor (Economic History Library)

Organization of the Course

This course consists of two parts. The first part (approx. 4-5 weeks) consists of weekly introductory lectures and is aimed at providing the students with a sound basis of the empirical methods in preparation for the second part, which is a blocked seminar due to be held on June 8th, 2018 on the campus. Students will replicate the analysis in recent research articles and will summarize the results of the replication in a report as well as present them in the seminar part. The replication part has to be done in groups of two to three students.

Course Requirements

Econometrics at the level of Econometrics: Regression Analysis

Course Outline & Readings

The course introduces students to state-of-the art methods in program evaluation. After the course, students should have a sound formal and intuitive understanding of the methods discussed. The course aims at enabling students to critically assess existing studies and to evaluate policies themselves. Since the course discusses advanced empirical methods, students need to have good knowledge of econometrics at the level of Econometrics (MSc) or Econometrics: Regression Analysis (MQE).

Topics:

  1. The Evaluation and Selection Problems
  2. Treatment Parameters
  3. Social Experiments
  4. Differences-in-Differences and Synthetic Control Groups
  5. Matching
  6. Instrumental Variables
  7. Regression Discontinuity

 

The course makes use of the following textbook plus recent research articles:

Imbens, G. and D. Rubin (2015): Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press