Microeconometric Methods for Big Data

What it is about

You will learn modern microeconometric methods for empirical research with large data sets. The module teaches both theoretical foundations (e.g., asymptotics, OLS/2SLS/GMM) and practical procedures for estimation and prediction (e.g., Ridge, Lasso, decision trees, Random/Causal Forests). In the end you can apply these methods, assess the underlying assumptions, and interpret econometric results meaningfully.

What you will be able to do

• apply state-of-the-art econometric methods
• understand technical conditions and assumptions of the models
• assess the limitations of the methods in applications
• interpret econometric results professionally
• use knowledge to improve decision-making processes

What the module consists of

• Vorlesungconveys the theoretical foundations and concepts of microeconometrics
• integrierte Übungenpractical application of the methods and deepening of the lecture material
• übungssheet zur Selbstbearbeitungindividual exercise for consolidation, followed by discussion in the session

Teaching method

• Vorlesungprovide a building-block understanding of microeconometric methods
• integrierte Übungenpractical application and practice of the methods in the course context
• Übungssheet zur Einzelbearbeitungindependent practice and opportunity to improve the final grade