Embedded Files
Lectures
Lectures
The course offers an application-oriented introduction to modern empirical economic research. The focus is on linear regression analysis. We deal with the central assumptions, the interpretation as well as diverse application possibilities of linear regression based on current studies and questions from applied research. The course aims to provide students with the methodological basics to be able to critically evaluate empirical studies and independently conduct their empirical projects.
This course provides an introduction to causal inference. We will primarily be concerned with how and when we can make causal claims from empirical research. In the lecture, we will discuss statistical techniques and the necessary assumptions to make causal statements. In the tutorials, we will learn these techniques by actually implementing them and discussing the plausibility of the assumptions. After this class, you should understand and be able to apply the standard set of causal inference tools in the social sciences. These include randomized experiments, matching, instrumental variables, regression discontinuity designs, fixed effects regressions, and differences-in-differences.
The lecture provides an introduction to "Data Analytics" for economists. The first part of the lecture covers the basics of programming in R (loops, functions, if-then conditions, etc.) as well as collecting and managing data. The second part is devoted to the preparation of data. In the third part of the lecture, we cover the analysis and visualization of data. The integrated exercises discuss application examples from business and economics.
This course aims to equip students with the basic data skills needed throughout their degree course and beyond. The course covers basic practical skills in gathering, preparing, and manipulating digital data for research purposes. Practical exercises and case studies from current research projects will deepen the concepts taught and train students in the basics of programming with data. The first part of the course covers theoretical concepts in handling digital data by focusing on different data structures and data formats. In the second part, students will learn to manipulate and prepare digital data for research purposes. Students will acquire basic programming skills with R to apply these practices to real-world datasets.
Tutorials
Tutorials
Causal Analysis
Causal Analysis
HS19
Applied Statistics and Econometrics
Applied Statistics and Econometrics
HS18, FS18, HS17, FS17
Statistics
Statistics
FS18, FS17
Mathematics
Mathematics
HS16
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