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Michael Brottrager

Universitätsassistent mit Master
Forschungsinteressen: Applied Microeconometrics, Labour Economics and Public Sector Economics

Curriculum Vitae

Academic Position (pre-doc)
Department of Economics, Johannes Kepler University.
 

Ph.D. Student, Department of Economics, Johannes Kepler University

MSc. Economics, Vienna University of Economics and Business.
Thesis: Endogenous Treatment Effects For Count Data Models With Endogenous Participation - Analysing
Intensive And Extensive Margins Of Smoking Behaviour.
Supervisor: Univ. Prof. Dr. Jesus Crespo Cuaresma

BSc. Economics and Socioeconomics, Vienna University of Economics and Business.
Thesis: Greed And Grievance In Civil War: Economic Motivations In Third Party Intervention Decisions.
Supervisor: Univ. Prof. Dr. Jesus Crespo Cuaresma
 

Lehrveranstaltungen

Entry requirements

There are no formal requirements, and students from all fields are welcome. However, a basic understanding of econometrics and statistical programming at the level of KS Empirical Economics and/or KS Intermediate Econometrics is necessary. Students who did not take these classes are urged to learn the material prior to the start of the semester.

 

Objectives

Students learn advanced concepts in programming and data management using the statistical software package R. Upon successful completion, students are capable to handle R and understand data management at a level required for the subsequent classes in the JKU econometrics curriculum (e.g., KS/IK Treatment Evaluation, KS Microeconometrics, etc).

 

Content

We start by discussing how to set up projects, arrange codes, and how to work with R objects. Based on these preliminaries, we will cover topics in data management (specifically how to combine, reorganize, and clean data), programming (e.g., how to use loops), as well as data analysis and visualization. A specific emphasis is placed on econometrics and big data. Most examples and exercises will be based on exemplary real-world data from the Austrian Social Security Database. We will discuss several particularities in handling big data sets throughout the course of the lecture.

 

Style

The course will be taught in class but will also be live-streamed. Please make sure to download the following software packages before the start of the course:

If you're already comfortable with R/RStudio try and install the TinyTex package (Latex Distribution tailored to R) using the following command line: tinytex::install_tinytex().

Grading

50% problem sets, 50% take-home exam/ term-paper at the end of the semester.

Lehre (aktuelles Semester)

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