• Students are able read and understand empirical papers and journal articles that make use of the concepts and methods that are introduced in the course
• Students are able to make use of econometric models in your own academic work, for example in analyses needed for your master’s thesis
• The students know the difference between descriptive and causal analysis. They have an overview of the possibilities of causal identification.

• Recall the basic identifying assumptions of ordinary least-square estimation
• be able to interpret and critically evaluate outcomes of regression analyses
• be able to interpret empirical model specifications
• understand the concept of a counterfactual situation and causality in empirical work

• recall statistical properties of regession methods
• illustrate the identifying assumptions that are necessary to interpretate empirical results
• racall and interpret different model specifications

Course Topics

• Basic statistical concepts (samples, probability distributions and their moments, ...)
• Estimation and testing
• Cross sectional data, time series, and panel data
• Descriptive analysis and graphical representations
• The linear regression model
• The multivariate regression model -- model specification
• Introduction to causal identification