Qualification Goals/Learning Outcomes/Competencies:
This course provides analytical skills that are essential in many professional contexts, including policy analysis, consulting and business decision-making. After completing the course, you should be able to:
- Assess whether observed relationships between variables can be interpreted as causal
- Analyze causal relationships using appropriate statistical software (Stata)
- Understand and apply the most commonly used econometric methods for causal analysis
Content:
“Econometrics is the original data science”, in the words of Nobel Prize laureate Joshua Angrist.
Econometricians analyze data with a particular focus on determining whether relationships between variables are causal. Such knowledge is essential for informed decision-making in policy and business contexts.
In this course, you will learn the core set of econometric tools for causal data analysis. All methods are based on regressions and employ a rigorous approach to identify causal effects, often centered on the concept of so-called natural experiments. The course covers methods such as OLS, instrumental variables, regression discontinuity designs, fixed effects models and difference-in-differences.
You will gain practical experience by applying these methods on databases using Stata.
Applicability of the Module:
As an elective module in M. Sc. in Quantitative Decision Making in Economics and Management.
As an elective module in M. Sc. in International Economics and Public Policy.
Language:
English
Literature:
Selected chapters from Cunningham (2021), Stock & Watson (2019) and Huntington-Klein (2021)