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Instructors

Module 7600
Applied Microeconometrics and Impact Analysis

Lecturers

Prof. Dr. Johannes Sauer, jo.sauer@tum.de
Dr. Amer Ait Sidhoum, amer.ait-sidhoum@tum.de

Aim

At the end of the course, students shall be:

Skills: quantitative analysis, conceptual thinking, econometrics, impact analysis

Contents

Outline

  1. Introduction
    • Fundamentals: correlation, causality and randomization
    • Selection bias and other sources of endogeneity
    • The role of experimental and quasi-experimental research design
  2. Cross-sectional, panel regression and theoretical assumptions
    • Ordinary Least Squares (OLS)
    • Fixed and Random Effects
    • Gauss-Markov assumptions
    • Identification strategies
  3. Propensity Score Matching
    • Logit/Probit regression
    • General Procedure: Propensity Score and Matching Methods
    • Exercise
  4. Instrumental Variables
    • Selection of Suitable Instruments
    • Estimation and Interpretation
    • Testing Instrument Strength
    • Exercise
  5. Difference-in-Difference Estimation
    • General Setting and Assumptions
    • Exercise
  6. Regression Discontinuity Design
    • Methodology and Assumptions
    • Testing the Assumptions
    • Exercise
  7. Synthetic Control Groups
    • General Setting and Assumptions
    • Exercise

Teaching forms (Workload in hours)

1 week block seminar with lecture and exercise (40h), Exercises (50h), Total (90h)

Examination: Homework assignments based on the students own dataset

Grading: In-class and homework assignments, where a minimum score of 50% is required to pass the module

Credit points: 3 CP

Recommendation:
Graduate-level training in econometrics
or Module 2500 AAE-1: Linear Models and Panel Data
or Module 6500 AAE-2: Limited Dependent Variable (LDV) / Choice models

Language: English

Literature

Textbooks

Fundamentals

Instrumental Variables

Difference-in-Difference Estimation

Propensity Score Matching

Regression Discontinuity Design

Synthetic Control Groups

Software: R

During the course, all exercises can be conducted in R (Studio) or Stata. Support will mainly be given in R and our handouts will be in R. A short introduction to R and R Studio will be provided, but we suggest you familiarize yourself with the software prior to the course.
http://www.r-tutor.com/r-introduction
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf