040012 UE Econometric Programming in Economics (MA) (2023S)

NOTE: The course will be offered in hybrid format (in-class and via live stream) if the class size exceeds the maximum capacity of the computer labs.

Content:

This course provides students with the skill set to conduct econometric analysis. Building upon the introductory course of econometrics, students will get a deeper understanding of the relevant methods used in contemporary empirical analysis and they will learn how to implement these methods using the statistical software R. While the techniques and methods are also introduced and discussed in class, the emphasis of the course is on their application in R.Typical questions of this course are, among others: What methods are available to estimate coefficients and standard errors of a specific regression model? What are the implications of missing data or measurement error? Which techniques are suitable for the analysis of duration data? How can (local) average treatment effects be estimated? What methods are available to exploit quasi-experimental variation?

Course goals:

At the end of the course, students will be familiar with the relevant techniques and tools used for empirical analysis in applied economics. This will allow them to critically read, understand and replicate empirical research. For a given research question and dataset, students will be able to choose appropriate econometric methods and conduct empirical analysis using R.

Prerequisites:

- Knowledge of statistics and applied econometrics (MA course “Introductory Econometrics” or equivalent)
- Basic knowledge of R or similar statistical software