This course gives students a general introduction into statistical and econometric methods, and teaches them how to apply these methods in R. We start with a general introduction into statistical programming in R and commence with in-depth discussion of data visualization and regression analysis. The final part of the course focuses on causal inference and teaches students how to think about endogeneity-induced biases in DAGs and the Potential Outcomes Framework, and how to apply Instrumental Variables, Difference-in-Differences, and Regression Discontinuity Designs to tackle these biases.

We discussed recent publications in economic history and persistence studies that try to explain global income inequalities. The course started with the Neolithic and Industrial Revolutions as the most significant events that induced divergence in growth rates, before discussing different geographic, institutional and cultural explanations for income differences across countries. All topics involved in-depth discussions of the empirical methods used by the authors along with replications of seminal works in the area.

Our institute offers this course to prepare students for the mandatory Macroeconomics Exam in the Bachelor studies. In nine lectures, we repeat the main contents of the main lecture. Among other things, we repeat the analysis of business cycles with the IS-LM modell, discuss the Philipps Curve and its implications for monetary policy, and discuss the main assumptions of the Solow growth model.