Professional

I attended Anja Neundorf’s Longitudinal Data Analysis at Colchester Campus in Essex. This is a two-week intense course which covers panel data analysis (with Stata and R).

Methods of Modern Causal Analysis Based on Observational was given by Michael Gebel at University of Bamberg. I attended an additional thrilling early course on Data Visualization. The central aim of this course is to empower participants to think about causality and to apply new tools of modern causal analysis in their own research.

Machine Learning with R was my first full online course during the pandemic given by Brett Lantz.

Causal Inference in Observational StudiesSecond online course. By Krisztian Posch and Thiago Oliveira . This course as been another great experience. After setting the stage with the potential outcome framework and experimental design we jumped into details of matching, difference in difference, fixed effects, synthetic control, instrument variables, causal mediation, regression discontinuity (sharp and fuzzy). You may find all these topics covered in a well designed quantitative master course. Kristian and Thiago published using several of these methods. Thus it was possible to really go into the details of everything, if you had questions.

I am working on commuting behaviour and various impact on commuter’s life. I use household panel data and explore strategies to identify causal effects.

In Specific Plans as Health Intervention – An Experimental Study I wanted to bring my statistical skills to a new level. Thus I decided to conduct a randomized online experiment in order to analyze implementation intentions, a self-regulation strategy to promote healthy eating habits.

I created a 2×2 design (2 groups at 2 times) with two slightly different interventions. Since there was no real control group, it is comparable to an A/B-test in marketing (which I later learned). I was fascinated by experimental studies which I got in touch with in my psychology studies. I proudly recruited 1226 people who participated at both interviews.

If you’ve done something like this, you understand what a one-man-army is about. I refined my knowledge about empirical studies, from study design, sample size planning, pilot studies, recruiting, data management, hypothesis testing, t-tests etc. etc.

The paper was graded with excellent at the chair of statistics.

In Subjective Well-Being and Personality I explored happiness as a proxy for utility and how it depends upon big five personality traits. For the empirical analysis I retrieved data from ALLBUS, a german cross-sectional study.

At this stage, I was absolutely fascinated by questions such as: What is utility? How to measure it? Why optimize utility? Is there an optimal level of happiness? Why can’t we increase happiness (positive feeling) infinitely? Do we need a balance of positive and negative feelings? Should government interfer with happiness of its citizens? What can ecah individual do in order to increase one’s happiness? If personality affects happiness and personality is stable trait, is happiness deterministic?

I had no background in econometrics, programming or academic writing. Thus, with help of my friends, I learned everything from scratch in this first paper. From data management, statistics, regression to referencing. I used R and LaTeX.

The paper was graded with excellent at the chair of econometrics.

The Seminar in Applied Economics: Happiness and Discrimination is about empirical analysis of questionnaire data (SOEP) to answer questions from the field of happiness research and labour market discrimination. Students are given the opportunity to work on a personal term project under intense supervision.

The analysis is done with help of the statistical package R and results are reported in an R Markdown. In this course students learn and practice critical, statistical thinking based on complex rectangular panel data. I offer students the opportunity to become data fluent and learn major skills that they can use in their academic and business career.

Find out more: http://seminars-in-applied-economics.de/

The course covers economics of integration, some repetition of microeconomics and macroeconomic foundation as well as repetition of mathematical and statistical skills.

The course covers basic theories of multinational enterprises and related empirical research. Topics include internationalisation strategies of firms, the analysis of trade costs, offshoring, as well as productivity and labour market effects of multinational firms’ activities. The course is structured into two weekly lectures and one weekly exercise class. Each exercise class is accompanied by a weekly home assignment (more on that below).

The course extends the set of models covered in basic microeconomics by considering elements of behavioural economics, uncertainty, information asymmetries, and market failure. The course goes beyond what is considered standard microeconomic theory and covers areas of microeconomics that are pretty cool and interesting.

In math II and introduced about 50 international bachelor students to the fundamentals of analysis. I designed all exercises.

In math I was in full charge of the tutorial and introduced about 50 international bachelor students to the fundamentals of linear algebra and optimization. I designed all exercises.

In PL I introduced about 40 bachelor students to linear optimization problems and the corresonding alogrithms, e.g. simplex.